Overview

Dataset statistics

Number of variables30
Number of observations8632
Missing cells8677
Missing cells (%)3.4%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory10.4 MiB
Average record size in memory1.2 KiB

Variable types

Numeric14
Categorical13
URL2
Boolean1

Alerts

type has constant value "track"Constant
error has constant value "{'status': 404, 'message': 'analysis not found'}"Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
artist_name has a high cardinality: 3357 distinct valuesHigh cardinality
track_name has a high cardinality: 8314 distinct valuesHigh cardinality
hash has a high cardinality: 8629 distinct valuesHigh cardinality
id has a high cardinality: 8517 distinct valuesHigh cardinality
uri has a high cardinality: 8517 distinct valuesHigh cardinality
id_hash has a high cardinality: 8629 distinct valuesHigh cardinality
album has a high cardinality: 5278 distinct valuesHigh cardinality
release_date has a high cardinality: 2160 distinct valuesHigh cardinality
isrc has a high cardinality: 8519 distinct valuesHigh cardinality
energy is highly overall correlated with danceability and 4 other fieldsHigh correlation
loudness is highly overall correlated with danceability and 3 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
danceability is highly overall correlated with energy and 3 other fieldsHigh correlation
valence is highly overall correlated with danceability and 1 other fieldsHigh correlation
tempo is highly overall correlated with danceability and 2 other fieldsHigh correlation
error has 8629 (> 99.9%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
track_name is uniformly distributedUniform
hash is uniformly distributedUniform
id is uniformly distributedUniform
uri is uniformly distributedUniform
id_hash is uniformly distributedUniform
isrc is uniformly distributedUniform
plays has 212 (2.5%) zerosZeros
key has 1017 (11.8%) zerosZeros
instrumentalness has 1164 (13.5%) zerosZeros
popularity has 166 (1.9%) zerosZeros

Reproduction

Analysis started2022-11-29 20:54:42.323914
Analysis finished2022-11-29 20:55:59.056638
Duration1 minute and 16.73 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct8630
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4555.8437
Minimum1
Maximum9082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:55:59.263669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile467.55
Q12281.75
median4568.5
Q36826.25
95-th percentile8627.45
Maximum9082
Range9081
Interquartile range (IQR)4544.5

Descriptive statistics

Standard deviation2622.1758
Coefficient of variation (CV)0.57556315
Kurtosis-1.2019863
Mean4555.8437
Median Absolute Deviation (MAD)2273
Skewness-0.0079613308
Sum39326043
Variance6875805.7
MonotonicityNot monotonic
2022-11-29T21:55:59.595967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8871 2
 
< 0.1%
8066 2
 
< 0.1%
6077 1
 
< 0.1%
6069 1
 
< 0.1%
6070 1
 
< 0.1%
6071 1
 
< 0.1%
6072 1
 
< 0.1%
6073 1
 
< 0.1%
6076 1
 
< 0.1%
1 1
 
< 0.1%
Other values (8620) 8620
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
9082 1
< 0.1%
9081 1
< 0.1%
9080 1
< 0.1%
9079 1
< 0.1%
9078 1
< 0.1%
9077 1
< 0.1%
9076 1
< 0.1%
9075 1
< 0.1%
9074 1
< 0.1%
9073 1
< 0.1%

artist_name
Categorical

Distinct3357
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size644.6 KiB
Justice
 
99
Radiohead
 
74
Tame Impala
 
73
IDLES
 
63
The Beatles
 
55
Other values (3352)
8268 

Length

Max length51
Median length33
Mean length10.705978
Min length2

Characters and Unicode

Total characters92414
Distinct characters300
Distinct categories17 ?
Distinct scripts9 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2308 ?
Unique (%)26.7%

Sample

1st row!!!
2nd row00110100 01010100
3rd row03 Greedo
4th row070 Shake
5th row070 Shake

Common Values

ValueCountFrequency (%)
Justice 99
 
1.1%
Radiohead 74
 
0.9%
Tame Impala 73
 
0.8%
IDLES 63
 
0.7%
The Beatles 55
 
0.6%
The Strokes 54
 
0.6%
King Krule 48
 
0.6%
Tycho 47
 
0.5%
Allah-Las 47
 
0.5%
Helado Negro 45
 
0.5%
Other values (3347) 8027
93.0%

Length

2022-11-29T21:56:00.121281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 813
 
5.1%
justice 99
 
0.6%
98
 
0.6%
black 90
 
0.6%
boys 83
 
0.5%
impala 74
 
0.5%
radiohead 74
 
0.5%
tame 73
 
0.5%
of 72
 
0.5%
king 66
 
0.4%
Other values (4578) 14303
90.3%

Most occurring characters

ValueCountFrequency (%)
e 8539
 
9.2%
7213
 
7.8%
a 7113
 
7.7%
o 5251
 
5.7%
n 5037
 
5.5%
r 4867
 
5.3%
i 4816
 
5.2%
s 4278
 
4.6%
l 4066
 
4.4%
t 3318
 
3.6%
Other values (290) 37916
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65837
71.2%
Uppercase Letter 18346
 
19.9%
Space Separator 7213
 
7.8%
Other Punctuation 394
 
0.4%
Decimal Number 230
 
0.2%
Other Letter 205
 
0.2%
Dash Punctuation 153
 
0.2%
Math Symbol 16
 
< 0.1%
Currency Symbol 6
 
< 0.1%
Modifier Letter 3
 
< 0.1%
Other values (7) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 13
 
6.3%
ي 8
 
3.9%
ل 6
 
2.9%
ن 5
 
2.4%
5
 
2.4%
س 4
 
2.0%
ر 4
 
2.0%
و 4
 
2.0%
3
 
1.5%
ق 3
 
1.5%
Other values (124) 150
73.2%
Lowercase Letter
ValueCountFrequency (%)
e 8539
13.0%
a 7113
10.8%
o 5251
 
8.0%
n 5037
 
7.7%
r 4867
 
7.4%
i 4816
 
7.3%
s 4278
 
6.5%
l 4066
 
6.2%
t 3318
 
5.0%
h 2649
 
4.0%
Other values (74) 15903
24.2%
Uppercase Letter
ValueCountFrequency (%)
T 1584
 
8.6%
S 1389
 
7.6%
B 1358
 
7.4%
M 1245
 
6.8%
A 1056
 
5.8%
D 1021
 
5.6%
C 963
 
5.2%
P 814
 
4.4%
J 806
 
4.4%
K 793
 
4.3%
Other values (36) 7317
39.9%
Other Punctuation
ValueCountFrequency (%)
. 197
50.0%
& 87
22.1%
' 51
 
12.9%
, 22
 
5.6%
! 13
 
3.3%
/ 11
 
2.8%
" 6
 
1.5%
? 3
 
0.8%
: 2
 
0.5%
1
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 60
26.1%
1 34
14.8%
3 33
14.3%
9 26
11.3%
7 24
 
10.4%
8 18
 
7.8%
5 14
 
6.1%
6 9
 
3.9%
2 8
 
3.5%
4 4
 
1.7%
Math Symbol
ValueCountFrequency (%)
+ 13
81.2%
> 3
 
18.8%
Modifier Letter
ValueCountFrequency (%)
ـ 2
66.7%
1
33.3%
Spacing Mark
ValueCountFrequency (%)
ि 1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
7213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 153
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 6
100.0%
Nonspacing Mark
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84115
91.0%
Common 8022
 
8.7%
Han 76
 
0.1%
Arabic 71
 
0.1%
Cyrillic 50
 
0.1%
Hangul 35
 
< 0.1%
Greek 18
 
< 0.1%
Katakana 16
 
< 0.1%
Devanagari 11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8539
 
10.2%
a 7113
 
8.5%
o 5251
 
6.2%
n 5037
 
6.0%
r 4867
 
5.8%
i 4816
 
5.7%
s 4278
 
5.1%
l 4066
 
4.8%
t 3318
 
3.9%
h 2649
 
3.1%
Other values (79) 34181
40.6%
Han
ValueCountFrequency (%)
5
 
6.6%
3
 
3.9%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (51) 51
67.1%
Common
ValueCountFrequency (%)
7213
89.9%
. 197
 
2.5%
- 153
 
1.9%
& 87
 
1.1%
0 60
 
0.7%
' 51
 
0.6%
1 34
 
0.4%
3 33
 
0.4%
9 26
 
0.3%
7 24
 
0.3%
Other values (23) 144
 
1.8%
Hangul
ValueCountFrequency (%)
3
 
8.6%
3
 
8.6%
3
 
8.6%
2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (18) 18
51.4%
Cyrillic
ValueCountFrequency (%)
н 5
 
10.0%
р 4
 
8.0%
а 4
 
8.0%
и 4
 
8.0%
о 4
 
8.0%
к 3
 
6.0%
т 3
 
6.0%
в 2
 
4.0%
м 2
 
4.0%
у 2
 
4.0%
Other values (17) 17
34.0%
Arabic
ValueCountFrequency (%)
ا 13
18.3%
ي 8
11.3%
ل 6
 
8.5%
ن 5
 
7.0%
س 4
 
5.6%
ر 4
 
5.6%
و 4
 
5.6%
ق 3
 
4.2%
ى 3
 
4.2%
ة 2
 
2.8%
Other values (14) 19
26.8%
Greek
ValueCountFrequency (%)
α 3
16.7%
η 2
11.1%
ς 2
11.1%
σ 1
 
5.6%
μ 1
 
5.6%
π 1
 
5.6%
ο 1
 
5.6%
ύ 1
 
5.6%
ζ 1
 
5.6%
Β 1
 
5.6%
Other values (4) 4
22.2%
Katakana
ValueCountFrequency (%)
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (4) 4
25.0%
Devanagari
ValueCountFrequency (%)
2
18.2%
ि 1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91901
99.4%
None 248
 
0.3%
CJK 76
 
0.1%
Arabic 73
 
0.1%
Cyrillic 50
 
0.1%
Hangul 35
 
< 0.1%
Katakana 18
 
< 0.1%
Devanagari 11
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8539
 
9.3%
7213
 
7.8%
a 7113
 
7.7%
o 5251
 
5.7%
n 5037
 
5.5%
r 4867
 
5.3%
i 4816
 
5.2%
s 4278
 
4.7%
l 4066
 
4.4%
t 3318
 
3.6%
Other values (69) 37403
40.7%
None
ValueCountFrequency (%)
ü 41
16.5%
é 31
 
12.5%
ö 26
 
10.5%
Ä 17
 
6.9%
ä 13
 
5.2%
ø 11
 
4.4%
ò 9
 
3.6%
í 9
 
3.6%
á 7
 
2.8%
å 6
 
2.4%
Other values (42) 78
31.5%
Arabic
ValueCountFrequency (%)
ا 13
17.8%
ي 8
 
11.0%
ل 6
 
8.2%
ن 5
 
6.8%
س 4
 
5.5%
ر 4
 
5.5%
و 4
 
5.5%
ق 3
 
4.1%
ى 3
 
4.1%
ة 2
 
2.7%
Other values (15) 21
28.8%
CJK
ValueCountFrequency (%)
5
 
6.6%
3
 
3.9%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (51) 51
67.1%
Cyrillic
ValueCountFrequency (%)
н 5
 
10.0%
р 4
 
8.0%
а 4
 
8.0%
и 4
 
8.0%
о 4
 
8.0%
к 3
 
6.0%
т 3
 
6.0%
в 2
 
4.0%
м 2
 
4.0%
у 2
 
4.0%
Other values (17) 17
34.0%
Hangul
ValueCountFrequency (%)
3
 
8.6%
3
 
8.6%
3
 
8.6%
2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (18) 18
51.4%
Devanagari
ValueCountFrequency (%)
2
18.2%
ि 1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Katakana
ValueCountFrequency (%)
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (6) 6
33.3%
Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%

track_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8314
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size693.9 KiB
Intro
 
9
You and I
 
7
Bones
 
5
Gloria
 
5
Echoes
 
5
Other values (8309)
8601 

Length

Max length102
Median length67
Mean length15.725093
Min length1

Characters and Unicode

Total characters135739
Distinct characters550
Distinct categories20 ?
Distinct scripts12 ?
Distinct blocks15 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8064 ?
Unique (%)93.4%

Sample

1st rowSo We Can Fuck
2nd row0000 871 0003
3rd rowSubstance (We Woke Up)
4th rowGuilty Conscience - Tame Impala Remix
5th rowGuilty Conscience - Tame Impala Remix Extended

Common Values

ValueCountFrequency (%)
Intro 9
 
0.1%
You and I 7
 
0.1%
Bones 5
 
0.1%
Gloria 5
 
0.1%
Echoes 5
 
0.1%
You 4
 
< 0.1%
Home 4
 
< 0.1%
Untitled 4
 
< 0.1%
Dreams 4
 
< 0.1%
Smile 4
 
< 0.1%
Other values (8304) 8581
99.4%

Length

2022-11-29T21:56:00.537013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1035
 
4.1%
the 757
 
3.0%
you 315
 
1.2%
remix 295
 
1.2%
i 258
 
1.0%
feat 257
 
1.0%
in 247
 
1.0%
a 243
 
1.0%
to 234
 
0.9%
of 215
 
0.9%
Other values (7761) 21429
84.7%

Most occurring characters

ValueCountFrequency (%)
16653
 
12.3%
e 13025
 
9.6%
a 7897
 
5.8%
o 7621
 
5.6%
i 7485
 
5.5%
n 7032
 
5.2%
t 6145
 
4.5%
r 6020
 
4.4%
s 4892
 
3.6%
l 4446
 
3.3%
Other values (540) 54523
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90398
66.6%
Uppercase Letter 22877
 
16.9%
Space Separator 16653
 
12.3%
Other Punctuation 1532
 
1.1%
Decimal Number 1366
 
1.0%
Dash Punctuation 955
 
0.7%
Close Punctuation 679
 
0.5%
Open Punctuation 679
 
0.5%
Other Letter 467
 
0.3%
Final Punctuation 59
 
< 0.1%
Other values (10) 74
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 23
 
4.9%
ل 16
 
3.4%
ي 10
 
2.1%
ر 10
 
2.1%
م 7
 
1.5%
6
 
1.3%
ن 6
 
1.3%
י 5
 
1.1%
و 5
 
1.1%
ب 5
 
1.1%
Other values (297) 374
80.1%
Lowercase Letter
ValueCountFrequency (%)
e 13025
14.4%
a 7897
 
8.7%
o 7621
 
8.4%
i 7485
 
8.3%
n 7032
 
7.8%
t 6145
 
6.8%
r 6020
 
6.7%
s 4892
 
5.4%
l 4446
 
4.9%
h 3323
 
3.7%
Other values (108) 22512
24.9%
Uppercase Letter
ValueCountFrequency (%)
S 2032
 
8.9%
T 1841
 
8.0%
M 1560
 
6.8%
L 1314
 
5.7%
A 1292
 
5.6%
B 1288
 
5.6%
D 1254
 
5.5%
R 1250
 
5.5%
C 1103
 
4.8%
I 1092
 
4.8%
Other values (46) 8851
38.7%
Other Punctuation
ValueCountFrequency (%)
. 494
32.2%
' 379
24.7%
, 278
18.1%
& 120
 
7.8%
/ 93
 
6.1%
! 68
 
4.4%
? 52
 
3.4%
: 19
 
1.2%
" 16
 
1.0%
* 4
 
0.3%
Other values (5) 9
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 323
23.6%
2 284
20.8%
1 272
19.9%
9 113
 
8.3%
5 72
 
5.3%
3 69
 
5.1%
6 64
 
4.7%
7 63
 
4.6%
4 60
 
4.4%
8 46
 
3.4%
Nonspacing Mark
ValueCountFrequency (%)
3
15.8%
3
15.8%
̈ 3
15.8%
2
10.5%
́ 2
10.5%
̊ 2
10.5%
̄ 1
 
5.3%
1
 
5.3%
̨ 1
 
5.3%
̌ 1
 
5.3%
Close Punctuation
ValueCountFrequency (%)
) 664
97.8%
] 13
 
1.9%
2
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 664
97.8%
[ 13
 
1.9%
2
 
0.3%
Final Punctuation
ValueCountFrequency (%)
54
91.5%
4
 
6.8%
» 1
 
1.7%
Currency Symbol
ValueCountFrequency (%)
$ 15
75.0%
4
 
20.0%
¢ 1
 
5.0%
Initial Punctuation
ValueCountFrequency (%)
4
66.7%
« 1
 
16.7%
1
 
16.7%
Math Symbol
ValueCountFrequency (%)
+ 4
50.0%
2
25.0%
= 2
25.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
50.0%
´ 1
25.0%
` 1
25.0%
Spacing Mark
ValueCountFrequency (%)
2
50.0%
ि 1
25.0%
1
25.0%
Other Symbol
ValueCountFrequency (%)
° 1
33.3%
1
33.3%
1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 952
99.7%
3
 
0.3%
Modifier Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
16653
100.0%
Format
ValueCountFrequency (%)
4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 113054
83.3%
Common 21973
 
16.2%
Cyrillic 195
 
0.1%
Han 178
 
0.1%
Arabic 120
 
0.1%
Hangul 104
 
0.1%
Devanagari 38
 
< 0.1%
Hebrew 32
 
< 0.1%
Greek 28
 
< 0.1%
Inherited 10
 
< 0.1%
Other values (2) 7
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
6
 
3.4%
4
 
2.2%
3
 
1.7%
3
 
1.7%
3
 
1.7%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
Other values (146) 149
83.7%
Latin
ValueCountFrequency (%)
e 13025
 
11.5%
a 7897
 
7.0%
o 7621
 
6.7%
i 7485
 
6.6%
n 7032
 
6.2%
t 6145
 
5.4%
r 6020
 
5.3%
s 4892
 
4.3%
l 4446
 
3.9%
h 3323
 
2.9%
Other values (107) 45168
40.0%
Hangul
ValueCountFrequency (%)
4
 
3.8%
3
 
2.9%
3
 
2.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (77) 80
76.9%
Common
ValueCountFrequency (%)
16653
75.8%
- 952
 
4.3%
) 664
 
3.0%
( 664
 
3.0%
. 494
 
2.2%
' 379
 
1.7%
0 323
 
1.5%
2 284
 
1.3%
, 278
 
1.3%
1 272
 
1.2%
Other values (45) 1010
 
4.6%
Cyrillic
ValueCountFrequency (%)
а 22
 
11.3%
е 18
 
9.2%
н 17
 
8.7%
т 14
 
7.2%
о 13
 
6.7%
л 11
 
5.6%
с 11
 
5.6%
и 9
 
4.6%
р 8
 
4.1%
в 7
 
3.6%
Other values (30) 65
33.3%
Arabic
ValueCountFrequency (%)
ا 23
19.2%
ل 16
13.3%
ي 10
 
8.3%
ر 10
 
8.3%
م 7
 
5.8%
ن 6
 
5.0%
و 5
 
4.2%
ب 5
 
4.2%
ع 5
 
4.2%
ء 4
 
3.3%
Other values (17) 29
24.2%
Devanagari
ValueCountFrequency (%)
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
1
 
2.6%
Other values (13) 13
34.2%
Greek
ValueCountFrequency (%)
ι 3
 
10.7%
α 3
 
10.7%
ο 3
 
10.7%
π 2
 
7.1%
ρ 2
 
7.1%
ε 2
 
7.1%
υ 2
 
7.1%
ί 1
 
3.6%
μ 1
 
3.6%
ς 1
 
3.6%
Other values (8) 8
28.6%
Hebrew
ValueCountFrequency (%)
י 5
15.6%
ו 4
12.5%
ה 4
12.5%
ל 4
12.5%
ש 2
 
6.2%
ח 2
 
6.2%
ר 2
 
6.2%
ד 2
 
6.2%
א 2
 
6.2%
ן 1
 
3.1%
Other values (4) 4
12.5%
Inherited
ValueCountFrequency (%)
̈ 3
30.0%
́ 2
20.0%
̊ 2
20.0%
̄ 1
 
10.0%
̨ 1
 
10.0%
̌ 1
 
10.0%
Katakana
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Hiragana
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134575
99.1%
None 400
 
0.3%
Cyrillic 195
 
0.1%
CJK 177
 
0.1%
Arabic 120
 
0.1%
Hangul 104
 
0.1%
Punctuation 71
 
0.1%
Devanagari 38
 
< 0.1%
Hebrew 32
 
< 0.1%
Diacriticals 10
 
< 0.1%
Other values (5) 17
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16653
 
12.4%
e 13025
 
9.7%
a 7897
 
5.9%
o 7621
 
5.7%
i 7485
 
5.6%
n 7032
 
5.2%
t 6145
 
4.6%
r 6020
 
4.5%
s 4892
 
3.6%
l 4446
 
3.3%
Other values (76) 53359
39.7%
Punctuation
ValueCountFrequency (%)
54
76.1%
4
 
5.6%
4
 
5.6%
4
 
5.6%
3
 
4.2%
1
 
1.4%
1
 
1.4%
None
ValueCountFrequency (%)
ä 47
 
11.8%
é 47
 
11.8%
ü 46
 
11.5%
ö 18
 
4.5%
í 18
 
4.5%
ı 17
 
4.2%
å 16
 
4.0%
ç 10
 
2.5%
Ü 9
 
2.2%
ó 9
 
2.2%
Other values (82) 163
40.8%
Arabic
ValueCountFrequency (%)
ا 23
19.2%
ل 16
13.3%
ي 10
 
8.3%
ر 10
 
8.3%
م 7
 
5.8%
ن 6
 
5.0%
و 5
 
4.2%
ب 5
 
4.2%
ع 5
 
4.2%
ء 4
 
3.3%
Other values (17) 29
24.2%
Cyrillic
ValueCountFrequency (%)
а 22
 
11.3%
е 18
 
9.2%
н 17
 
8.7%
т 14
 
7.2%
о 13
 
6.7%
л 11
 
5.6%
с 11
 
5.6%
и 9
 
4.6%
р 8
 
4.1%
в 7
 
3.6%
Other values (30) 65
33.3%
CJK
ValueCountFrequency (%)
6
 
3.4%
4
 
2.3%
3
 
1.7%
3
 
1.7%
3
 
1.7%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
Other values (145) 148
83.6%
Hebrew
ValueCountFrequency (%)
י 5
15.6%
ו 4
12.5%
ה 4
12.5%
ל 4
12.5%
ש 2
 
6.2%
ח 2
 
6.2%
ר 2
 
6.2%
ד 2
 
6.2%
א 2
 
6.2%
ן 1
 
3.1%
Other values (4) 4
12.5%
Currency Symbols
ValueCountFrequency (%)
4
100.0%
Devanagari
ValueCountFrequency (%)
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
1
 
2.6%
Other values (13) 13
34.2%
Hangul
ValueCountFrequency (%)
4
 
3.8%
3
 
2.9%
3
 
2.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (77) 80
76.9%
Diacriticals
ValueCountFrequency (%)
̈ 3
30.0%
́ 2
20.0%
̊ 2
20.0%
̄ 1
 
10.0%
̨ 1
 
10.0%
̌ 1
 
10.0%
Arrows
ValueCountFrequency (%)
2
100.0%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Hiragana
ValueCountFrequency (%)
1
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
50.0%
1
50.0%

plays
Real number (ℝ)

Distinct77
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.344115
Minimum0
Maximum4.6249728
Zeros212
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:00.942077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.69314718
Q10.69314718
median1.0986123
Q31.7917595
95-th percentile3.0445224
Maximum4.6249728
Range4.6249728
Interquartile range (IQR)1.0986123

Descriptive statistics

Standard deviation0.79170935
Coefficient of variation (CV)0.58901905
Kurtosis1.3066806
Mean1.344115
Median Absolute Deviation (MAD)0.40546511
Skewness1.2067841
Sum11602.401
Variance0.6268037
MonotonicityNot monotonic
2022-11-29T21:56:01.327179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6931471806 2967
34.4%
1.098612289 1815
21.0%
1.386294361 779
 
9.0%
1.609437912 614
 
7.1%
1.791759469 441
 
5.1%
1.945910149 337
 
3.9%
0 212
 
2.5%
2.079441542 201
 
2.3%
2.197224577 166
 
1.9%
2.302585093 127
 
1.5%
Other values (67) 973
 
11.3%
ValueCountFrequency (%)
0 212
 
2.5%
0.6931471806 2967
34.4%
1.098612289 1815
21.0%
1.386294361 779
 
9.0%
1.609437912 614
 
7.1%
1.791759469 441
 
5.1%
1.945910149 337
 
3.9%
2.079441542 201
 
2.3%
2.197224577 166
 
1.9%
2.302585093 127
 
1.5%
ValueCountFrequency (%)
4.624972813 1
< 0.1%
4.615120517 1
< 0.1%
4.543294782 1
< 0.1%
4.510859507 2
< 0.1%
4.442651256 1
< 0.1%
4.406719247 1
< 0.1%
4.394449155 2
< 0.1%
4.356708827 1
< 0.1%
4.33073334 1
< 0.1%
4.317488114 1
< 0.1%

hash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8629
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293
 
4
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
 
1
a26b27cdf8dcc9542472c7dfd05391226484c5c7012abfcf034e46c1d85d832c
 
1
4a7055c0e664822def486e17d35a974849335f614e0aef41d07a16299d914233
 
1
e30e5241af7634499476f163134ffdf27e446eecf2e24c003e9238816e14ce05
 
1
Other values (8624)
8624 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters552448
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8628 ?
Unique (%)> 99.9%

Sample

1st row40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
2nd row8fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd133694
3rd row5139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a3
4th row2b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560
5th row283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4

Common Values

ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717 1
 
< 0.1%
a26b27cdf8dcc9542472c7dfd05391226484c5c7012abfcf034e46c1d85d832c 1
 
< 0.1%
4a7055c0e664822def486e17d35a974849335f614e0aef41d07a16299d914233 1
 
< 0.1%
e30e5241af7634499476f163134ffdf27e446eecf2e24c003e9238816e14ce05 1
 
< 0.1%
109ed5f8afde5cd1bc165cd97eed3f75edf90c25f8593ccd2978f91c3923ce53 1
 
< 0.1%
8f87a75b4b93d20bb7805ed50ab934bfc37cf7c870b9a1bf58221469fde2b21b 1
 
< 0.1%
17642e3381c64c168a4e136533cafc23f76142bc7bbd665f6f37dc3caf1928ee 1
 
< 0.1%
8f24e91a23390e1f140111f08173787dc44d3ab001c3bc154c9e7074f3db0059 1
 
< 0.1%
f95a4dacab9a612b22f6fd16c4a40b053165bf9f819b7b70499d848539c3357e 1
 
< 0.1%
Other values (8619) 8619
99.8%

Length

2022-11-29T21:56:01.670480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
2fc16d43297a76e743a6b4f55fb9dbb490fdac0c66277cffed8ee7f3aa1ee6ed 1
 
< 0.1%
2b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560 1
 
< 0.1%
283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4 1
 
< 0.1%
ab6360e389e3f5d1195762455462751a2cafccfcb6ad56ddbf025a8c999b0b73 1
 
< 0.1%
3c8a3f0b83fbbf1283e05bc8e8359c3b2e0167a2846aeae1ab32a3b823b9b7dd 1
 
< 0.1%
5a4fe1451e61705a1b59338ee79fb383eb3efd7f57d41dc32eb2f56f1422a446 1
 
< 0.1%
d02f429f5cf8b05621bad75db5f28706f81333c12848cb95bf6dbab3b3b270ba 1
 
< 0.1%
cbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c85 1
 
< 0.1%
118e08bd62d0adc42bb4ca154793d8f464f408b3c20e581606ea2c8862d8eff8 1
 
< 0.1%
Other values (8619) 8619
99.8%

Most occurring characters

ValueCountFrequency (%)
7 34796
 
6.3%
b 34700
 
6.3%
2 34637
 
6.3%
5 34612
 
6.3%
c 34605
 
6.3%
f 34599
 
6.3%
1 34587
 
6.3%
8 34582
 
6.3%
3 34563
 
6.3%
e 34480
 
6.2%
Other values (6) 206287
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 345281
62.5%
Lowercase Letter 207167
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 34796
10.1%
2 34637
10.0%
5 34612
10.0%
1 34587
10.0%
8 34582
10.0%
3 34563
10.0%
4 34451
10.0%
9 34414
10.0%
0 34336
9.9%
6 34303
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 34700
16.7%
c 34605
16.7%
f 34599
16.7%
e 34480
16.6%
a 34443
16.6%
d 34340
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 345281
62.5%
Latin 207167
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
7 34796
10.1%
2 34637
10.0%
5 34612
10.0%
1 34587
10.0%
8 34582
10.0%
3 34563
10.0%
4 34451
10.0%
9 34414
10.0%
0 34336
9.9%
6 34303
9.9%
Latin
ValueCountFrequency (%)
b 34700
16.7%
c 34605
16.7%
f 34599
16.7%
e 34480
16.6%
a 34443
16.6%
d 34340
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 552448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 34796
 
6.3%
b 34700
 
6.3%
2 34637
 
6.3%
5 34612
 
6.3%
c 34605
 
6.3%
f 34599
 
6.3%
1 34587
 
6.3%
8 34582
 
6.3%
3 34563
 
6.3%
e 34480
 
6.2%
Other values (6) 206287
37.3%

danceability
Real number (ℝ)

Distinct896
Distinct (%)10.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.56791406
Minimum0.0565
Maximum0.984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:02.098460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0565
5-th percentile0.229
Q10.458
median0.586
Q30.696
95-th percentile0.826
Maximum0.984
Range0.9275
Interquartile range (IQR)0.238

Descriptive statistics

Standard deviation0.17670599
Coefficient of variation (CV)0.31114917
Kurtosis-0.12072063
Mean0.56791406
Median Absolute Deviation (MAD)0.118
Skewness-0.46142306
Sum4900.5304
Variance0.031225006
MonotonicityNot monotonic
2022-11-29T21:56:02.457395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.633 37
 
0.4%
0.625 30
 
0.3%
0.641 29
 
0.3%
0.545 29
 
0.3%
0.623 28
 
0.3%
0.534 28
 
0.3%
0.705 28
 
0.3%
0.647 28
 
0.3%
0.646 27
 
0.3%
0.63 27
 
0.3%
Other values (886) 8338
96.6%
ValueCountFrequency (%)
0.0565 2
< 0.1%
0.0611 1
< 0.1%
0.0612 1
< 0.1%
0.0616 1
< 0.1%
0.0627 1
< 0.1%
0.0634 1
< 0.1%
0.0639 1
< 0.1%
0.0645 1
< 0.1%
0.0646 1
< 0.1%
0.065 1
< 0.1%
ValueCountFrequency (%)
0.984 2
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 1
 
< 0.1%
0.975 1
 
< 0.1%
0.972 1
 
< 0.1%
0.969 1
 
< 0.1%
0.967 2
< 0.1%
0.966 3
< 0.1%

energy
Real number (ℝ)

Distinct1147
Distinct (%)13.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.6272399
Minimum0.000431
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:02.879271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.000431
5-th percentile0.177
Q10.487
median0.661
Q30.808
95-th percentile0.939
Maximum1
Range0.999569
Interquartile range (IQR)0.321

Descriptive statistics

Standard deviation0.2296025
Coefficient of variation (CV)0.36605212
Kurtosis-0.021091931
Mean0.6272399
Median Absolute Deviation (MAD)0.159
Skewness-0.69102382
Sum5412.4531
Variance0.052717307
MonotonicityNot monotonic
2022-11-29T21:56:03.272268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.859 25
 
0.3%
0.781 24
 
0.3%
0.792 24
 
0.3%
0.576 23
 
0.3%
0.759 23
 
0.3%
0.724 23
 
0.3%
0.84 22
 
0.3%
0.596 22
 
0.3%
0.659 22
 
0.3%
0.882 21
 
0.2%
Other values (1137) 8400
97.3%
ValueCountFrequency (%)
0.000431 1
< 0.1%
0.000603 1
< 0.1%
0.00105 1
< 0.1%
0.00134 1
< 0.1%
0.00142 1
< 0.1%
0.00151 1
< 0.1%
0.00155 1
< 0.1%
0.00166 1
< 0.1%
0.00172 1
< 0.1%
0.0019 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.999 1
 
< 0.1%
0.998 5
0.1%
0.997 2
 
< 0.1%
0.996 3
< 0.1%
0.995 5
0.1%
0.994 6
0.1%
0.993 4
< 0.1%
0.992 2
 
< 0.1%
0.991 3
< 0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2823039
Minimum0
Maximum11
Zeros1017
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:03.624732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5851182
Coefficient of variation (CV)0.67870351
Kurtosis-1.2914473
Mean5.2823039
Median Absolute Deviation (MAD)3
Skewness0.011569
Sum45581
Variance12.853072
MonotonicityNot monotonic
2022-11-29T21:56:04.048391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1017
11.8%
7 987
11.4%
2 937
10.9%
9 923
10.7%
1 797
9.2%
11 742
8.6%
4 693
8.0%
5 685
7.9%
6 576
6.7%
10 509
5.9%
Other values (2) 763
8.8%
ValueCountFrequency (%)
0 1017
11.8%
1 797
9.2%
2 937
10.9%
3 263
 
3.0%
4 693
8.0%
5 685
7.9%
6 576
6.7%
7 987
11.4%
8 500
5.8%
9 923
10.7%
ValueCountFrequency (%)
11 742
8.6%
10 509
5.9%
9 923
10.7%
8 500
5.8%
7 987
11.4%
6 576
6.7%
5 685
7.9%
4 693
8.0%
3 263
 
3.0%
2 937
10.9%

loudness
Real number (ℝ)

Distinct6067
Distinct (%)70.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-8.9560783
Minimum-46.847
Maximum2.358
Zeros0
Zeros (%)0.0%
Negative8626
Negative (%)99.9%
Memory size134.9 KiB
2022-11-29T21:56:04.449705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-46.847
5-th percentile-18.2244
Q1-10.188
median-7.694
Q3-5.903
95-th percentile-3.8208
Maximum2.358
Range49.205
Interquartile range (IQR)4.285

Descriptive statistics

Standard deviation5.3929273
Coefficient of variation (CV)-0.60215276
Kurtosis9.7207856
Mean-8.9560783
Median Absolute Deviation (MAD)2.061
Skewness-2.732738
Sum-77282
Variance29.083665
MonotonicityNot monotonic
2022-11-29T21:56:04.854972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.043 7
 
0.1%
-5.174 5
 
0.1%
-7.081 5
 
0.1%
-6.52 5
 
0.1%
-6.312 5
 
0.1%
-7.159 5
 
0.1%
-7.441 5
 
0.1%
-7.415 5
 
0.1%
-8.172 5
 
0.1%
-8.055 5
 
0.1%
Other values (6057) 8577
99.4%
ValueCountFrequency (%)
-46.847 1
< 0.1%
-42.291 1
< 0.1%
-42.013 1
< 0.1%
-41.748 1
< 0.1%
-39.904 1
< 0.1%
-39.69 1
< 0.1%
-39.154 1
< 0.1%
-38.656 1
< 0.1%
-38.605 1
< 0.1%
-38.523 1
< 0.1%
ValueCountFrequency (%)
2.358 1
< 0.1%
2.357 1
< 0.1%
1.342 1
< 0.1%
-0.578 1
< 0.1%
-0.657 1
< 0.1%
-0.737 1
< 0.1%
-0.738 1
< 0.1%
-0.74 1
< 0.1%
-0.91 1
< 0.1%
-1.269 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size573.2 KiB
1.0
5276 
0.0
3353 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25887
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5276
61.1%
0.0 3353
38.8%
(Missing) 3
 
< 0.1%

Length

2022-11-29T21:56:05.426457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T21:56:05.879412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5276
61.1%
0.0 3353
38.9%

Most occurring characters

ValueCountFrequency (%)
0 11982
46.3%
. 8629
33.3%
1 5276
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17258
66.7%
Other Punctuation 8629
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11982
69.4%
1 5276
30.6%
Other Punctuation
ValueCountFrequency (%)
. 8629
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25887
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11982
46.3%
. 8629
33.3%
1 5276
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11982
46.3%
. 8629
33.3%
1 5276
20.4%

speechiness
Real number (ℝ)

Distinct1141
Distinct (%)13.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.078456536
Minimum0.021859334
Maximum0.67650954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:06.187007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.021859334
5-th percentile0.027323296
Q10.033724869
median0.043059489
Q30.071203759
95-th percentile0.27155269
Maximum0.67650954
Range0.65465021
Interquartile range (IQR)0.03747889

Descriptive statistics

Standard deviation0.10080008
Coefficient of variation (CV)1.2847888
Kurtosis17.510344
Mean0.078456536
Median Absolute Deviation (MAD)0.012239347
Skewness3.8903961
Sum677.00145
Variance0.010160656
MonotonicityNot monotonic
2022-11-29T21:56:06.563899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0339182182 40
 
0.5%
0.03178932249 39
 
0.5%
0.02907324749 37
 
0.4%
0.03130484983 35
 
0.4%
0.02926749768 35
 
0.4%
0.03159556159 35
 
0.4%
0.02936460863 34
 
0.4%
0.03546366428 33
 
0.4%
0.03584965289 33
 
0.4%
0.0283930745 33
 
0.4%
Other values (1131) 8275
95.9%
ValueCountFrequency (%)
0.02185933435 1
 
< 0.1%
0.02225060893 1
 
< 0.1%
0.02234840366 1
 
< 0.1%
0.02254396443 1
 
< 0.1%
0.02273948697 2
< 0.1%
0.0228372339 1
 
< 0.1%
0.0233258253 1
 
< 0.1%
0.02342351494 1
 
< 0.1%
0.02352119504 1
 
< 0.1%
0.0236188656 3
< 0.1%
ValueCountFrequency (%)
0.6765095394 3
 
< 0.1%
0.6760010217 6
0.1%
0.6754922453 3
 
< 0.1%
0.6749832099 6
0.1%
0.6744739153 6
0.1%
0.6739643611 8
0.1%
0.6734545472 6
0.1%
0.6729444732 6
0.1%
0.672434139 5
0.1%
0.6719235441 3
 
< 0.1%

acousticness
Real number (ℝ)

Distinct2841
Distinct (%)32.9%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.20998401
Minimum1.1999993 × 10-6
Maximum0.69114518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:06.984488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.1999993 × 10-6
5-th percentile0.00034634002
Q10.016168581
median0.11689375
Q30.37569295
95-th percentile0.65315814
Maximum0.69114518
Range0.69114398
Interquartile range (IQR)0.35952437

Descriptive statistics

Standard deviation0.22218486
Coefficient of variation (CV)1.0581037
Kurtosis-0.73314219
Mean0.20998401
Median Absolute Deviation (MAD)0.11406775
Skewness0.81663026
Sum1811.952
Variance0.049366112
MonotonicityNot monotonic
2022-11-29T21:56:07.332875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1552928844 17
 
0.2%
0.1414995623 17
 
0.2%
0.1663615372 16
 
0.2%
0.1034587084 16
 
0.2%
0.1213322852 16
 
0.2%
0.1168937515 15
 
0.2%
0.1133286853 15
 
0.2%
0.01232374969 15
 
0.2%
0.09803374027 14
 
0.2%
0.6891391592 14
 
0.2%
Other values (2831) 8474
98.2%
ValueCountFrequency (%)
1.19999928 × 10-61
< 0.1%
1.279999181 × 10-61
< 0.1%
1.299999155 × 10-61
< 0.1%
1.559998783 × 10-61
< 0.1%
1.769998434 × 10-61
< 0.1%
2.379997168 × 10-61
< 0.1%
2.729996274 × 10-61
< 0.1%
2.829995996 × 10-61
< 0.1%
2.98999553 × 10-61
< 0.1%
3.209994848 × 10-61
< 0.1%
ValueCountFrequency (%)
0.6911451779 6
0.1%
0.6906440503 7
0.1%
0.6901426715 11
0.1%
0.6896410412 13
0.2%
0.6891391592 14
0.2%
0.6886370251 5
 
0.1%
0.6881346387 8
0.1%
0.6876319999 8
0.1%
0.6871291082 13
0.2%
0.6866259636 10
0.1%

instrumentalness
Real number (ℝ)

Distinct3190
Distinct (%)37.0%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.19683822
Minimum0
Maximum0.69264706
Zeros1164
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:08.025819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.6597066 × 10-5
median0.021957167
Q30.45234869
95-th percentile0.64553133
Maximum0.69264706
Range0.69264706
Interquartile range (IQR)0.4522721

Descriptive statistics

Standard deviation0.2522644
Coefficient of variation (CV)1.2815824
Kurtosis-1.0661999
Mean0.19683822
Median Absolute Deviation (MAD)0.021957167
Skewness0.81598869
Sum1698.517
Variance0.063637327
MonotonicityNot monotonic
2022-11-29T21:56:08.402117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1164
 
13.5%
0.6502396795 16
 
0.2%
0.6243328646 16
 
0.2%
0.6450068052 16
 
0.2%
0.6264730473 15
 
0.2%
0.6544063522 15
 
0.2%
0.6323350412 15
 
0.2%
0.6392188385 15
 
0.2%
0.6481498146 14
 
0.2%
0.6232610531 14
 
0.2%
Other values (3180) 7329
84.9%
ValueCountFrequency (%)
0 1164
13.5%
9.999995 × 10-72
 
< 0.1%
1.00999949 × 10-61
 
< 0.1%
1.02999947 × 10-63
 
< 0.1%
1.039999459 × 10-61
 
< 0.1%
1.049999449 × 10-61
 
< 0.1%
1.059999438 × 10-61
 
< 0.1%
1.069999428 × 10-65
 
0.1%
1.079999417 × 10-61
 
< 0.1%
1.089999406 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.6926470555 1
 
< 0.1%
0.6906440503 3
< 0.1%
0.6901426715 1
 
< 0.1%
0.6896410412 1
 
< 0.1%
0.6886370251 1
 
< 0.1%
0.6876319999 1
 
< 0.1%
0.6861225656 1
 
< 0.1%
0.6846108495 1
 
< 0.1%
0.6841064359 2
< 0.1%
0.6836017677 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct1249
Distinct (%)14.5%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.16664131
Minimum0.020586634
Maximum0.68712911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:08.822136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.020586634
5-th percentile0.062035391
Q10.092943746
median0.11689375
Q30.20945022
95-th percentile0.42592112
Maximum0.68712911
Range0.66654247
Interquartile range (IQR)0.11650648

Descriptive statistics

Standard deviation0.11704926
Coefficient of variation (CV)0.70240246
Kurtosis3.347212
Mean0.16664131
Median Absolute Deviation (MAD)0.035827336
Skewness1.8218379
Sum1437.9478
Variance0.01370053
MonotonicityNot monotonic
2022-11-29T21:56:09.240541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1052605107 123
 
1.4%
0.1043600153 112
 
1.3%
0.1034587084 111
 
1.3%
0.09984533497 105
 
1.2%
0.09712671073 102
 
1.2%
0.1007499031 101
 
1.2%
0.1016536537 101
 
1.2%
0.1025565883 99
 
1.1%
0.09803374027 96
 
1.1%
0.09621885774 94
 
1.1%
Other values (1239) 7585
87.9%
ValueCountFrequency (%)
0.02058663361 1
< 0.1%
0.02127213528 1
< 0.1%
0.02146790662 1
< 0.1%
0.02166363964 1
< 0.1%
0.0242046887 1
< 0.1%
0.02439988682 1
< 0.1%
0.02596010167 1
< 0.1%
0.02644717001 1
< 0.1%
0.02673929719 1
< 0.1%
0.02761516703 1
< 0.1%
ValueCountFrequency (%)
0.6871291082 1
< 0.1%
0.6866259636 1
< 0.1%
0.6856189141 1
< 0.1%
0.6846108495 1
< 0.1%
0.6836017677 1
< 0.1%
0.6820862332 1
< 0.1%
0.6810745993 1
< 0.1%
0.679555227 1
< 0.1%
0.6785410282 2
< 0.1%
0.6775257997 1
< 0.1%

valence
Real number (ℝ)

Distinct1256
Distinct (%)14.6%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.47789839
Minimum0
Maximum0.982
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:09.657748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07244
Q10.278
median0.476
Q30.677
95-th percentile0.903
Maximum0.982
Range0.982
Interquartile range (IQR)0.399

Descriptive statistics

Standard deviation0.25360844
Coefficient of variation (CV)0.53067439
Kurtosis-0.96651981
Mean0.47789839
Median Absolute Deviation (MAD)0.199
Skewness0.076608837
Sum4123.7852
Variance0.06431724
MonotonicityNot monotonic
2022-11-29T21:56:10.063533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.963 22
 
0.3%
0.424 22
 
0.3%
0.961 21
 
0.2%
0.389 20
 
0.2%
0.611 19
 
0.2%
0.497 18
 
0.2%
0.414 18
 
0.2%
0.529 18
 
0.2%
0.567 18
 
0.2%
0.67 18
 
0.2%
Other values (1246) 8435
97.7%
ValueCountFrequency (%)
0 2
< 0.1%
0.0147 1
< 0.1%
0.0166 1
< 0.1%
0.0274 1
< 0.1%
0.0283 1
< 0.1%
0.0291 1
< 0.1%
0.0298 1
< 0.1%
0.03 1
< 0.1%
0.0303 1
< 0.1%
0.0304 1
< 0.1%
ValueCountFrequency (%)
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 1
 
< 0.1%
0.979 2
 
< 0.1%
0.978 2
 
< 0.1%
0.977 2
 
< 0.1%
0.976 3
< 0.1%
0.975 3
< 0.1%
0.974 5
0.1%
0.973 3
< 0.1%

tempo
Real number (ℝ)

Distinct7443
Distinct (%)86.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean120.95884
Minimum35.862
Maximum219.921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:10.437807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum35.862
5-th percentile77.1806
Q1100.014
median120.002
Q3138.205
95-th percentile172.8334
Maximum219.921
Range184.059
Interquartile range (IQR)38.191

Descriptive statistics

Standard deviation28.444694
Coefficient of variation (CV)0.23516011
Kurtosis-0.23487479
Mean120.95884
Median Absolute Deviation (MAD)19.81
Skewness0.33727569
Sum1043753.8
Variance809.10061
MonotonicityNot monotonic
2022-11-29T21:56:10.837682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.006 9
 
0.1%
120.002 7
 
0.1%
120.044 6
 
0.1%
127.991 6
 
0.1%
109.992 6
 
0.1%
120.007 6
 
0.1%
99.999 5
 
0.1%
120.015 5
 
0.1%
140.008 5
 
0.1%
124.013 5
 
0.1%
Other values (7433) 8569
99.3%
ValueCountFrequency (%)
35.862 1
< 0.1%
42.749 1
< 0.1%
44.499 1
< 0.1%
45.01 1
< 0.1%
47.15 1
< 0.1%
49.251 1
< 0.1%
50.685 1
< 0.1%
50.786 1
< 0.1%
54.084 1
< 0.1%
54.797 1
< 0.1%
ValueCountFrequency (%)
219.921 1
< 0.1%
216.087 1
< 0.1%
216.053 1
< 0.1%
210.164 1
< 0.1%
210.028 1
< 0.1%
206.867 1
< 0.1%
206.733 1
< 0.1%
206.488 1
< 0.1%
206.247 1
< 0.1%
206.195 1
< 0.1%

type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.1 KiB
track
8632 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters43160
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrack
2nd rowtrack
3rd rowtrack
4th rowtrack
5th rowtrack

Common Values

ValueCountFrequency (%)
track 8632
100.0%

Length

2022-11-29T21:56:11.273999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T21:56:11.598234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
track 8632
100.0%

Most occurring characters

ValueCountFrequency (%)
t 8632
20.0%
r 8632
20.0%
a 8632
20.0%
c 8632
20.0%
k 8632
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43160
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 8632
20.0%
r 8632
20.0%
a 8632
20.0%
c 8632
20.0%
k 8632
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43160
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 8632
20.0%
r 8632
20.0%
a 8632
20.0%
c 8632
20.0%
k 8632
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 8632
20.0%
r 8632
20.0%
a 8632
20.0%
c 8632
20.0%
k 8632
20.0%

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8517
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size733.2 KiB
35tzxthMBglBMjmZ7Fn1hj
 
4
2a1iMaoWQ5MnvLFBDv4qkf
 
3
50fkJxrv0ZLTt9EHZGBOP7
 
3
7tDEl7uRnoGdi0SFghDCz6
 
2
5HYO0c3hEK8OawTw9wFPaV
 
2
Other values (8512)
8615 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters189838
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8409 ?
Unique (%)97.5%

Sample

1st row4knd2gQyr2DTRLfJDHcyMS
2nd row7ns3vcnzAxjCZVYwlwazah
3rd row2S8gTIectkC846PHdsAshC
4th row5i5fCpsnqDJ9AfeObgd0gW
5th row7qDUOLnOLYKTwzvCJDnYRf

Common Values

ValueCountFrequency (%)
35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
5HYO0c3hEK8OawTw9wFPaV 2
 
< 0.1%
2YpeDb67231RjR0MgVLzsG 2
 
< 0.1%
1hp7aHiWB6zxcpehJR7xRO 2
 
< 0.1%
1Cy0pPvxSMndbFe9p89q2J 2
 
< 0.1%
5ZdhUPFJV9Yd9zuwK5RITM 2
 
< 0.1%
09gysnJpfQ3ublBmJDfcEC 2
 
< 0.1%
Other values (8507) 8605
99.7%
(Missing) 3
 
< 0.1%

Length

2022-11-29T21:56:11.863438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35tzxthmbglbmjmz7fn1hj 4
 
< 0.1%
50fkjxrv0zltt9ehzgbop7 3
 
< 0.1%
2a1imaowq5mnvlfbdv4qkf 3
 
< 0.1%
4xxfq8fbhmigcygy4hv6rc 2
 
< 0.1%
6lozws7t3jqzz78unpgff9 2
 
< 0.1%
4oumlc67foplvqnue5c7kf 2
 
< 0.1%
4pqrsbt1uzjyd5ultfsom2 2
 
< 0.1%
78hago3mrvdg9si9gvkfs5 2
 
< 0.1%
18axbzpzbs8y3akgsxzjpb 2
 
< 0.1%
7ygpwy2qp3nbrxvkhvuhxy 2
 
< 0.1%
Other values (8507) 8605
99.7%

Most occurring characters

ValueCountFrequency (%)
3 4143
 
2.2%
1 4116
 
2.2%
4 4057
 
2.1%
0 4035
 
2.1%
2 4023
 
2.1%
6 4021
 
2.1%
5 3946
 
2.1%
7 3844
 
2.0%
p 3018
 
1.6%
L 3009
 
1.6%
Other values (52) 151626
79.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76225
40.2%
Uppercase Letter 75605
39.8%
Decimal Number 38008
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 3018
 
4.0%
i 2996
 
3.9%
c 2985
 
3.9%
y 2984
 
3.9%
z 2977
 
3.9%
b 2969
 
3.9%
t 2968
 
3.9%
x 2962
 
3.9%
h 2950
 
3.9%
j 2948
 
3.9%
Other values (16) 46468
61.0%
Uppercase Letter
ValueCountFrequency (%)
L 3009
 
4.0%
P 2996
 
4.0%
F 2987
 
4.0%
U 2960
 
3.9%
Z 2957
 
3.9%
D 2946
 
3.9%
Y 2940
 
3.9%
H 2933
 
3.9%
B 2927
 
3.9%
A 2924
 
3.9%
Other values (16) 46026
60.9%
Decimal Number
ValueCountFrequency (%)
3 4143
10.9%
1 4116
10.8%
4 4057
10.7%
0 4035
10.6%
2 4023
10.6%
6 4021
10.6%
5 3946
10.4%
7 3844
10.1%
9 2979
7.8%
8 2844
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 151830
80.0%
Common 38008
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 3018
 
2.0%
L 3009
 
2.0%
i 2996
 
2.0%
P 2996
 
2.0%
F 2987
 
2.0%
c 2985
 
2.0%
y 2984
 
2.0%
z 2977
 
2.0%
b 2969
 
2.0%
t 2968
 
2.0%
Other values (42) 121941
80.3%
Common
ValueCountFrequency (%)
3 4143
10.9%
1 4116
10.8%
4 4057
10.7%
0 4035
10.6%
2 4023
10.6%
6 4021
10.6%
5 3946
10.4%
7 3844
10.1%
9 2979
7.8%
8 2844
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4143
 
2.2%
1 4116
 
2.2%
4 4057
 
2.1%
0 4035
 
2.1%
2 4023
 
2.1%
6 4021
 
2.1%
5 3946
 
2.1%
7 3844
 
2.0%
p 3018
 
1.6%
L 3009
 
1.6%
Other values (52) 151626
79.9%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8517
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size851.2 KiB
spotify:track:35tzxthMBglBMjmZ7Fn1hj
 
4
spotify:track:2a1iMaoWQ5MnvLFBDv4qkf
 
3
spotify:track:50fkJxrv0ZLTt9EHZGBOP7
 
3
spotify:track:7tDEl7uRnoGdi0SFghDCz6
 
2
spotify:track:5HYO0c3hEK8OawTw9wFPaV
 
2
Other values (8512)
8615 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters310644
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8409 ?
Unique (%)97.5%

Sample

1st rowspotify:track:4knd2gQyr2DTRLfJDHcyMS
2nd rowspotify:track:7ns3vcnzAxjCZVYwlwazah
3rd rowspotify:track:2S8gTIectkC846PHdsAshC
4th rowspotify:track:5i5fCpsnqDJ9AfeObgd0gW
5th rowspotify:track:7qDUOLnOLYKTwzvCJDnYRf

Common Values

ValueCountFrequency (%)
spotify:track:35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
spotify:track:2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
spotify:track:50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
spotify:track:7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
spotify:track:5HYO0c3hEK8OawTw9wFPaV 2
 
< 0.1%
spotify:track:2YpeDb67231RjR0MgVLzsG 2
 
< 0.1%
spotify:track:1hp7aHiWB6zxcpehJR7xRO 2
 
< 0.1%
spotify:track:1Cy0pPvxSMndbFe9p89q2J 2
 
< 0.1%
spotify:track:5ZdhUPFJV9Yd9zuwK5RITM 2
 
< 0.1%
spotify:track:09gysnJpfQ3ublBmJDfcEC 2
 
< 0.1%
Other values (8507) 8605
99.7%
(Missing) 3
 
< 0.1%

Length

2022-11-29T21:56:12.139704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:35tzxthmbglbmjmz7fn1hj 4
 
< 0.1%
spotify:track:50fkjxrv0zltt9ehzgbop7 3
 
< 0.1%
spotify:track:2a1imaowq5mnvlfbdv4qkf 3
 
< 0.1%
spotify:track:4xxfq8fbhmigcygy4hv6rc 2
 
< 0.1%
spotify:track:6lozws7t3jqzz78unpgff9 2
 
< 0.1%
spotify:track:4oumlc67foplvqnue5c7kf 2
 
< 0.1%
spotify:track:4pqrsbt1uzjyd5ultfsom2 2
 
< 0.1%
spotify:track:78hago3mrvdg9si9gvkfs5 2
 
< 0.1%
spotify:track:18axbzpzbs8y3akgsxzjpb 2
 
< 0.1%
spotify:track:7ygpwy2qp3nbrxvkhvuhxy 2
 
< 0.1%
Other values (8507) 8605
99.7%

Most occurring characters

ValueCountFrequency (%)
t 20226
 
6.5%
: 17258
 
5.6%
p 11647
 
3.7%
i 11625
 
3.7%
c 11614
 
3.7%
y 11613
 
3.7%
s 11568
 
3.7%
f 11567
 
3.7%
k 11551
 
3.7%
a 11538
 
3.7%
Other values (53) 180437
58.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 179773
57.9%
Uppercase Letter 75605
24.3%
Decimal Number 38008
 
12.2%
Other Punctuation 17258
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 20226
 
11.3%
p 11647
 
6.5%
i 11625
 
6.5%
c 11614
 
6.5%
y 11613
 
6.5%
s 11568
 
6.4%
f 11567
 
6.4%
k 11551
 
6.4%
a 11538
 
6.4%
o 11532
 
6.4%
Other values (16) 55292
30.8%
Uppercase Letter
ValueCountFrequency (%)
L 3009
 
4.0%
P 2996
 
4.0%
F 2987
 
4.0%
U 2960
 
3.9%
Z 2957
 
3.9%
D 2946
 
3.9%
Y 2940
 
3.9%
H 2933
 
3.9%
B 2927
 
3.9%
A 2924
 
3.9%
Other values (16) 46026
60.9%
Decimal Number
ValueCountFrequency (%)
3 4143
10.9%
1 4116
10.8%
4 4057
10.7%
0 4035
10.6%
2 4023
10.6%
6 4021
10.6%
5 3946
10.4%
7 3844
10.1%
9 2979
7.8%
8 2844
7.5%
Other Punctuation
ValueCountFrequency (%)
: 17258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 255378
82.2%
Common 55266
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 20226
 
7.9%
p 11647
 
4.6%
i 11625
 
4.6%
c 11614
 
4.5%
y 11613
 
4.5%
s 11568
 
4.5%
f 11567
 
4.5%
k 11551
 
4.5%
a 11538
 
4.5%
o 11532
 
4.5%
Other values (42) 130897
51.3%
Common
ValueCountFrequency (%)
: 17258
31.2%
3 4143
 
7.5%
1 4116
 
7.4%
4 4057
 
7.3%
0 4035
 
7.3%
2 4023
 
7.3%
6 4021
 
7.3%
5 3946
 
7.1%
7 3844
 
7.0%
9 2979
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 20226
 
6.5%
: 17258
 
5.6%
p 11647
 
3.7%
i 11625
 
3.7%
c 11614
 
3.7%
y 11613
 
3.7%
s 11568
 
3.7%
f 11567
 
3.7%
k 11551
 
3.7%
a 11538
 
3.7%
Other values (53) 180437
58.1%
Distinct8517
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size1019.8 KiB
https://api.spotify.com/v1/tracks/35tzxthMBglBMjmZ7Fn1hj
 
4
https://api.spotify.com/v1/tracks/2a1iMaoWQ5MnvLFBDv4qkf
 
3
https://api.spotify.com/v1/tracks/50fkJxrv0ZLTt9EHZGBOP7
 
3
https://api.spotify.com/v1/tracks/7tDEl7uRnoGdi0SFghDCz6
 
2
https://api.spotify.com/v1/tracks/5HYO0c3hEK8OawTw9wFPaV
 
2
Other values (8512)
8615 
(Missing)
 
3
ValueCountFrequency (%)
https://api.spotify.com/v1/tracks/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
https://api.spotify.com/v1/tracks/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
https://api.spotify.com/v1/tracks/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
https://api.spotify.com/v1/tracks/7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
https://api.spotify.com/v1/tracks/5HYO0c3hEK8OawTw9wFPaV 2
 
< 0.1%
https://api.spotify.com/v1/tracks/2YpeDb67231RjR0MgVLzsG 2
 
< 0.1%
https://api.spotify.com/v1/tracks/1hp7aHiWB6zxcpehJR7xRO 2
 
< 0.1%
https://api.spotify.com/v1/tracks/1Cy0pPvxSMndbFe9p89q2J 2
 
< 0.1%
https://api.spotify.com/v1/tracks/5ZdhUPFJV9Yd9zuwK5RITM 2
 
< 0.1%
https://api.spotify.com/v1/tracks/09gysnJpfQ3ublBmJDfcEC 2
 
< 0.1%
Other values (8507) 8605
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
https 8629
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
api.spotify.com 8629
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
/v1/tracks/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
/v1/tracks/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
/v1/tracks/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
/v1/tracks/2wSZ6ynM06pJE8lroAhyy8 2
 
< 0.1%
/v1/tracks/6ta1rdmev5quOSBWe304cA 2
 
< 0.1%
/v1/tracks/0GDz2tPdh6FbxJxM75SQB4 2
 
< 0.1%
/v1/tracks/4cCoZML1dPIQxNjOwDmJGf 2
 
< 0.1%
/v1/tracks/4VXIryQMWpIdGgYR4TrjT1 2
 
< 0.1%
/v1/tracks/43zdsphuZLzwA9k4DJhU0I 2
 
< 0.1%
/v1/tracks/1V0mZgcXX5CAr1cGrlh6bz 2
 
< 0.1%
Other values (8507) 8605
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8629
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8629
> 99.9%
(Missing) 3
 
< 0.1%
Distinct8517
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size1.1 MiB
https://api.spotify.com/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj
 
4
https://api.spotify.com/v1/audio-analysis/2a1iMaoWQ5MnvLFBDv4qkf
 
3
https://api.spotify.com/v1/audio-analysis/50fkJxrv0ZLTt9EHZGBOP7
 
3
https://api.spotify.com/v1/audio-analysis/7tDEl7uRnoGdi0SFghDCz6
 
2
https://api.spotify.com/v1/audio-analysis/5HYO0c3hEK8OawTw9wFPaV
 
2
Other values (8512)
8615 
(Missing)
 
3
ValueCountFrequency (%)
https://api.spotify.com/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/5HYO0c3hEK8OawTw9wFPaV 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/2YpeDb67231RjR0MgVLzsG 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/1hp7aHiWB6zxcpehJR7xRO 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/1Cy0pPvxSMndbFe9p89q2J 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/5ZdhUPFJV9Yd9zuwK5RITM 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/09gysnJpfQ3ublBmJDfcEC 2
 
< 0.1%
Other values (8507) 8605
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
https 8629
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
api.spotify.com 8629
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
/v1/audio-analysis/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
/v1/audio-analysis/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
/v1/audio-analysis/2wSZ6ynM06pJE8lroAhyy8 2
 
< 0.1%
/v1/audio-analysis/6ta1rdmev5quOSBWe304cA 2
 
< 0.1%
/v1/audio-analysis/0GDz2tPdh6FbxJxM75SQB4 2
 
< 0.1%
/v1/audio-analysis/4cCoZML1dPIQxNjOwDmJGf 2
 
< 0.1%
/v1/audio-analysis/4VXIryQMWpIdGgYR4TrjT1 2
 
< 0.1%
/v1/audio-analysis/43zdsphuZLzwA9k4DJhU0I 2
 
< 0.1%
/v1/audio-analysis/1V0mZgcXX5CAr1cGrlh6bz 2
 
< 0.1%
Other values (8507) 8605
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8629
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8629
> 99.9%
(Missing) 3
 
< 0.1%

duration_ms
Real number (ℝ)

Distinct7800
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234376.87
Minimum15106
Maximum1172433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:12.441587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15106
5-th percentile132542.85
Q1185681.75
median220929.5
Q3267429.5
95-th percentile376838.85
Maximum1172433
Range1157327
Interquartile range (IQR)81747.75

Descriptive statistics

Standard deviation83463.05
Coefficient of variation (CV)0.35610617
Kurtosis13.821215
Mean234376.87
Median Absolute Deviation (MAD)39020
Skewness2.2592327
Sum2.0231411 × 109
Variance6.9660806 × 109
MonotonicityNot monotonic
2022-11-29T21:56:12.897367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192000 7
 
0.1%
174000 7
 
0.1%
200000 6
 
0.1%
228000 6
 
0.1%
180000 6
 
0.1%
196000 5
 
0.1%
186000 5
 
0.1%
160000 5
 
0.1%
213000 5
 
0.1%
191266 4
 
< 0.1%
Other values (7790) 8576
99.4%
ValueCountFrequency (%)
15106 1
< 0.1%
16545 1
< 0.1%
25575 1
< 0.1%
27293 1
< 0.1%
28813 1
< 0.1%
29754 1
< 0.1%
30000 1
< 0.1%
30373 1
< 0.1%
30826 1
< 0.1%
31546 1
< 0.1%
ValueCountFrequency (%)
1172433 1
< 0.1%
1142213 1
< 0.1%
1074213 1
< 0.1%
1059733 1
< 0.1%
1045533 1
< 0.1%
970464 1
< 0.1%
969864 1
< 0.1%
950210 1
< 0.1%
936739 1
< 0.1%
902000 1
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size573.2 KiB
4.0
7842 
3.0
 
560
5.0
 
153
1.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25887
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 7842
90.8%
3.0 560
 
6.5%
5.0 153
 
1.8%
1.0 74
 
0.9%
(Missing) 3
 
< 0.1%

Length

2022-11-29T21:56:13.307387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T21:56:13.633308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 7842
90.9%
3.0 560
 
6.5%
5.0 153
 
1.8%
1.0 74
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 8629
33.3%
0 8629
33.3%
4 7842
30.3%
3 560
 
2.2%
5 153
 
0.6%
1 74
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17258
66.7%
Other Punctuation 8629
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8629
50.0%
4 7842
45.4%
3 560
 
3.2%
5 153
 
0.9%
1 74
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 8629
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25887
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8629
33.3%
0 8629
33.3%
4 7842
30.3%
3 560
 
2.2%
5 153
 
0.6%
1 74
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8629
33.3%
0 8629
33.3%
4 7842
30.3%
3 560
 
2.2%
5 153
 
0.6%
1 74
 
0.3%

id_hash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8629
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293
 
4
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
 
1
a26b27cdf8dcc9542472c7dfd05391226484c5c7012abfcf034e46c1d85d832c
 
1
4a7055c0e664822def486e17d35a974849335f614e0aef41d07a16299d914233
 
1
e30e5241af7634499476f163134ffdf27e446eecf2e24c003e9238816e14ce05
 
1
Other values (8624)
8624 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters552448
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8628 ?
Unique (%)> 99.9%

Sample

1st row40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
2nd row8fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd133694
3rd row5139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a3
4th row2b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560
5th row283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4

Common Values

ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717 1
 
< 0.1%
a26b27cdf8dcc9542472c7dfd05391226484c5c7012abfcf034e46c1d85d832c 1
 
< 0.1%
4a7055c0e664822def486e17d35a974849335f614e0aef41d07a16299d914233 1
 
< 0.1%
e30e5241af7634499476f163134ffdf27e446eecf2e24c003e9238816e14ce05 1
 
< 0.1%
109ed5f8afde5cd1bc165cd97eed3f75edf90c25f8593ccd2978f91c3923ce53 1
 
< 0.1%
8f87a75b4b93d20bb7805ed50ab934bfc37cf7c870b9a1bf58221469fde2b21b 1
 
< 0.1%
17642e3381c64c168a4e136533cafc23f76142bc7bbd665f6f37dc3caf1928ee 1
 
< 0.1%
8f24e91a23390e1f140111f08173787dc44d3ab001c3bc154c9e7074f3db0059 1
 
< 0.1%
f95a4dacab9a612b22f6fd16c4a40b053165bf9f819b7b70499d848539c3357e 1
 
< 0.1%
Other values (8619) 8619
99.8%

Length

2022-11-29T21:56:13.917358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
2fc16d43297a76e743a6b4f55fb9dbb490fdac0c66277cffed8ee7f3aa1ee6ed 1
 
< 0.1%
2b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560 1
 
< 0.1%
283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4 1
 
< 0.1%
ab6360e389e3f5d1195762455462751a2cafccfcb6ad56ddbf025a8c999b0b73 1
 
< 0.1%
3c8a3f0b83fbbf1283e05bc8e8359c3b2e0167a2846aeae1ab32a3b823b9b7dd 1
 
< 0.1%
5a4fe1451e61705a1b59338ee79fb383eb3efd7f57d41dc32eb2f56f1422a446 1
 
< 0.1%
d02f429f5cf8b05621bad75db5f28706f81333c12848cb95bf6dbab3b3b270ba 1
 
< 0.1%
cbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c85 1
 
< 0.1%
118e08bd62d0adc42bb4ca154793d8f464f408b3c20e581606ea2c8862d8eff8 1
 
< 0.1%
Other values (8619) 8619
99.8%

Most occurring characters

ValueCountFrequency (%)
7 34796
 
6.3%
b 34700
 
6.3%
2 34637
 
6.3%
5 34612
 
6.3%
c 34605
 
6.3%
f 34599
 
6.3%
1 34587
 
6.3%
8 34582
 
6.3%
3 34563
 
6.3%
e 34480
 
6.2%
Other values (6) 206287
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 345281
62.5%
Lowercase Letter 207167
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 34796
10.1%
2 34637
10.0%
5 34612
10.0%
1 34587
10.0%
8 34582
10.0%
3 34563
10.0%
4 34451
10.0%
9 34414
10.0%
0 34336
9.9%
6 34303
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 34700
16.7%
c 34605
16.7%
f 34599
16.7%
e 34480
16.6%
a 34443
16.6%
d 34340
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 345281
62.5%
Latin 207167
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
7 34796
10.1%
2 34637
10.0%
5 34612
10.0%
1 34587
10.0%
8 34582
10.0%
3 34563
10.0%
4 34451
10.0%
9 34414
10.0%
0 34336
9.9%
6 34303
9.9%
Latin
ValueCountFrequency (%)
b 34700
16.7%
c 34605
16.7%
f 34599
16.7%
e 34480
16.6%
a 34443
16.6%
d 34340
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 552448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 34796
 
6.3%
b 34700
 
6.3%
2 34637
 
6.3%
5 34612
 
6.3%
c 34605
 
6.3%
f 34599
 
6.3%
1 34587
 
6.3%
8 34582
 
6.3%
3 34563
 
6.3%
e 34480
 
6.2%
Other values (6) 206287
37.3%

album
Categorical

Distinct5278
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Memory size706.5 KiB
Maschinen wie ich (Ungekürzt)
 
39
Inside In, Inside Out (15th Anniversary Deluxe)
 
21
The OOZ
 
19
Past Cloaks
 
19
Back to Mine: Jungle (DJ Mix)
 
18
Other values (5273)
8516 

Length

Max length169
Median length92
Mean length16.928753
Min length1

Characters and Unicode

Total characters146129
Distinct characters505
Distinct categories20 ?
Distinct scripts12 ?
Distinct blocks13 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4186 ?
Unique (%)48.5%

Sample

1st rowI'm Sick of This/So We Can Fuck
2nd row871
3rd rowSubstance (We Woke Up)
4th rowGuilty Conscience (Tame Impala Remix)
5th rowGuilty Conscience (Tame Impala Remix)

Common Values

ValueCountFrequency (%)
Maschinen wie ich (Ungekürzt) 39
 
0.5%
Inside In, Inside Out (15th Anniversary Deluxe) 21
 
0.2%
The OOZ 19
 
0.2%
Past Cloaks 19
 
0.2%
Back to Mine: Jungle (DJ Mix) 18
 
0.2%
Light Up Gold + Tally All The Things That You Broke 18
 
0.2%
Definitely Maybe (Deluxe Edition Remastered) 18
 
0.2%
A Cross The Universe 17
 
0.2%
Justice 17
 
0.2%
Woman Worldwide 17
 
0.2%
Other values (5268) 8429
97.6%

Length

2022-11-29T21:56:14.280923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 1011
 
4.0%
354
 
1.4%
of 319
 
1.3%
a 303
 
1.2%
in 272
 
1.1%
deluxe 263
 
1.0%
you 233
 
0.9%
to 216
 
0.9%
i 182
 
0.7%
edition 167
 
0.7%
Other values (6151) 22076
86.9%

Most occurring characters

ValueCountFrequency (%)
16764
 
11.5%
e 13889
 
9.5%
o 8153
 
5.6%
i 7831
 
5.4%
n 7751
 
5.3%
a 7717
 
5.3%
r 6893
 
4.7%
t 6362
 
4.4%
s 5894
 
4.0%
l 4874
 
3.3%
Other values (495) 60001
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97199
66.5%
Uppercase Letter 25710
 
17.6%
Space Separator 16764
 
11.5%
Other Punctuation 1871
 
1.3%
Decimal Number 1376
 
0.9%
Open Punctuation 1172
 
0.8%
Close Punctuation 1172
 
0.8%
Other Letter 466
 
0.3%
Dash Punctuation 253
 
0.2%
Final Punctuation 43
 
< 0.1%
Other values (10) 103
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 24
 
5.2%
ل 20
 
4.3%
ي 13
 
2.8%
م 12
 
2.6%
8
 
1.7%
و 8
 
1.7%
ن 7
 
1.5%
ر 7
 
1.5%
6
 
1.3%
ع 6
 
1.3%
Other values (261) 355
76.2%
Lowercase Letter
ValueCountFrequency (%)
e 13889
14.3%
o 8153
 
8.4%
i 7831
 
8.1%
n 7751
 
8.0%
a 7717
 
7.9%
r 6893
 
7.1%
t 6362
 
6.5%
s 5894
 
6.1%
l 4874
 
5.0%
u 3542
 
3.6%
Other values (100) 24293
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 2166
 
8.4%
T 2110
 
8.2%
A 1636
 
6.4%
M 1575
 
6.1%
D 1474
 
5.7%
I 1342
 
5.2%
R 1283
 
5.0%
B 1261
 
4.9%
C 1260
 
4.9%
L 1253
 
4.9%
Other values (50) 10350
40.3%
Other Punctuation
ValueCountFrequency (%)
. 512
27.4%
' 396
21.2%
, 300
16.0%
: 169
 
9.0%
/ 149
 
8.0%
& 123
 
6.6%
! 102
 
5.5%
? 62
 
3.3%
" 25
 
1.3%
# 11
 
0.6%
Other values (6) 22
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 306
22.2%
0 292
21.2%
2 283
20.6%
9 124
9.0%
6 73
 
5.3%
5 70
 
5.1%
4 63
 
4.6%
3 60
 
4.4%
8 54
 
3.9%
7 51
 
3.7%
Math Symbol
ValueCountFrequency (%)
+ 22
55.0%
~ 8
 
20.0%
> 3
 
7.5%
< 2
 
5.0%
= 2
 
5.0%
× 2
 
5.0%
÷ 1
 
2.5%
Nonspacing Mark
ValueCountFrequency (%)
3
30.0%
́ 2
20.0%
̊ 2
20.0%
2
20.0%
1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 1126
96.1%
[ 44
 
3.8%
1
 
0.1%
1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1126
96.1%
] 44
 
3.8%
1
 
0.1%
1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 248
98.0%
4
 
1.6%
1
 
0.4%
Spacing Mark
ValueCountFrequency (%)
ि 2
50.0%
1
25.0%
1
25.0%
Final Punctuation
ValueCountFrequency (%)
42
97.7%
1
 
2.3%
Currency Symbol
ValueCountFrequency (%)
4
57.1%
$ 3
42.9%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
16764
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 31
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
100.0%
Format
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
® 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 122664
83.9%
Common 22740
 
15.6%
Cyrillic 215
 
0.1%
Han 163
 
0.1%
Arabic 133
 
0.1%
Hangul 72
 
< 0.1%
Hebrew 38
 
< 0.1%
Katakana 37
 
< 0.1%
Greek 30
 
< 0.1%
Devanagari 27
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
8
 
4.9%
6
 
3.7%
3
 
1.8%
3
 
1.8%
3
 
1.8%
2
 
1.2%
2
 
1.2%
2
 
1.2%
2
 
1.2%
2
 
1.2%
Other values (117) 130
79.8%
Latin
ValueCountFrequency (%)
e 13889
 
11.3%
o 8153
 
6.6%
i 7831
 
6.4%
n 7751
 
6.3%
a 7717
 
6.3%
r 6893
 
5.6%
t 6362
 
5.2%
s 5894
 
4.8%
l 4874
 
4.0%
u 3542
 
2.9%
Other values (104) 49758
40.6%
Hangul
ValueCountFrequency (%)
3
 
4.2%
3
 
4.2%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
1
 
1.4%
1
 
1.4%
1
 
1.4%
Other values (53) 53
73.6%
Common
ValueCountFrequency (%)
16764
73.7%
( 1126
 
5.0%
) 1126
 
5.0%
. 512
 
2.3%
' 396
 
1.7%
1 306
 
1.3%
, 300
 
1.3%
0 292
 
1.3%
2 283
 
1.2%
- 248
 
1.1%
Other values (46) 1387
 
6.1%
Cyrillic
ValueCountFrequency (%)
а 20
 
9.3%
н 20
 
9.3%
е 19
 
8.8%
с 19
 
8.8%
и 15
 
7.0%
т 13
 
6.0%
о 12
 
5.6%
л 9
 
4.2%
р 8
 
3.7%
ы 7
 
3.3%
Other values (29) 73
34.0%
Katakana
ValueCountFrequency (%)
5
 
13.5%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
Other values (14) 14
37.8%
Arabic
ValueCountFrequency (%)
ا 24
18.0%
ل 20
15.0%
ي 13
9.8%
م 12
9.0%
و 8
 
6.0%
ن 7
 
5.3%
ر 7
 
5.3%
ع 6
 
4.5%
ب 5
 
3.8%
ق 5
 
3.8%
Other values (13) 26
19.5%
Hebrew
ValueCountFrequency (%)
ל 5
13.2%
ה 5
13.2%
ו 4
10.5%
א 4
10.5%
י 3
 
7.9%
ש 2
 
5.3%
ס 2
 
5.3%
ת 2
 
5.3%
ח 2
 
5.3%
נ 1
 
2.6%
Other values (8) 8
21.1%
Devanagari
ValueCountFrequency (%)
4
14.8%
3
11.1%
ि 2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (7) 7
25.9%
Greek
ValueCountFrequency (%)
α 4
13.3%
ι 4
13.3%
ρ 3
10.0%
ο 2
 
6.7%
ε 2
 
6.7%
θ 2
 
6.7%
η 2
 
6.7%
ν 2
 
6.7%
Ά 1
 
3.3%
π 1
 
3.3%
Other values (7) 7
23.3%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Inherited
ValueCountFrequency (%)
́ 2
50.0%
̊ 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144779
99.1%
None 589
 
0.4%
Cyrillic 215
 
0.1%
CJK 163
 
0.1%
Arabic 133
 
0.1%
Hangul 72
 
< 0.1%
Punctuation 55
 
< 0.1%
Katakana 44
 
< 0.1%
Hebrew 38
 
< 0.1%
Devanagari 27
 
< 0.1%
Other values (3) 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16764
 
11.6%
e 13889
 
9.6%
o 8153
 
5.6%
i 7831
 
5.4%
n 7751
 
5.4%
a 7717
 
5.3%
r 6893
 
4.8%
t 6362
 
4.4%
s 5894
 
4.1%
l 4874
 
3.4%
Other values (79) 58651
40.5%
None
ValueCountFrequency (%)
ü 159
27.0%
é 74
12.6%
ä 59
 
10.0%
ç 28
 
4.8%
ö 27
 
4.6%
Ç 14
 
2.4%
ğ 13
 
2.2%
ø 13
 
2.2%
å 12
 
2.0%
è 11
 
1.9%
Other values (77) 179
30.4%
Punctuation
ValueCountFrequency (%)
42
76.4%
4
 
7.3%
3
 
5.5%
2
 
3.6%
1
 
1.8%
1
 
1.8%
1
 
1.8%
1
 
1.8%
Arabic
ValueCountFrequency (%)
ا 24
18.0%
ل 20
15.0%
ي 13
9.8%
م 12
9.0%
و 8
 
6.0%
ن 7
 
5.3%
ر 7
 
5.3%
ع 6
 
4.5%
ب 5
 
3.8%
ق 5
 
3.8%
Other values (13) 26
19.5%
Cyrillic
ValueCountFrequency (%)
а 20
 
9.3%
н 20
 
9.3%
е 19
 
8.8%
с 19
 
8.8%
и 15
 
7.0%
т 13
 
6.0%
о 12
 
5.6%
л 9
 
4.2%
р 8
 
3.7%
ы 7
 
3.3%
Other values (29) 73
34.0%
CJK
ValueCountFrequency (%)
8
 
4.9%
6
 
3.7%
3
 
1.8%
3
 
1.8%
3
 
1.8%
2
 
1.2%
2
 
1.2%
2
 
1.2%
2
 
1.2%
2
 
1.2%
Other values (117) 130
79.8%
Hebrew
ValueCountFrequency (%)
ל 5
13.2%
ה 5
13.2%
ו 4
10.5%
א 4
10.5%
י 3
 
7.9%
ש 2
 
5.3%
ס 2
 
5.3%
ת 2
 
5.3%
ח 2
 
5.3%
נ 1
 
2.6%
Other values (8) 8
21.1%
Katakana
ValueCountFrequency (%)
5
 
11.4%
4
 
9.1%
3
 
6.8%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
Other values (16) 18
40.9%
Currency Symbols
ValueCountFrequency (%)
4
100.0%
Devanagari
ValueCountFrequency (%)
4
14.8%
3
11.1%
ि 2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (7) 7
25.9%
Hangul
ValueCountFrequency (%)
3
 
4.2%
3
 
4.2%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
1
 
1.4%
1
 
1.4%
1
 
1.4%
Other values (53) 53
73.6%
Diacriticals
ValueCountFrequency (%)
́ 2
50.0%
̊ 2
50.0%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

release_date
Categorical

Distinct2160
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size630.5 KiB
2020-02-28
 
47
2021-03-26
 
45
2021-08-27
 
43
2019-09-13
 
43
2006-01-01
 
42
Other values (2155)
8412 

Length

Max length10
Median length10
Mean length9.7925162
Min length4

Characters and Unicode

Total characters84529
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1082 ?
Unique (%)12.5%

Sample

1st row2020-05-01
2nd row2020-12-25
3rd row2021-02-03
4th row2020-07-24
5th row2020-07-24

Common Values

ValueCountFrequency (%)
2020-02-28 47
 
0.5%
2021-03-26 45
 
0.5%
2021-08-27 43
 
0.5%
2019-09-13 43
 
0.5%
2006-01-01 42
 
0.5%
2021-02-26 41
 
0.5%
2019-10-18 41
 
0.5%
2021-01-15 40
 
0.5%
2019-06-26 39
 
0.5%
2021-04-30 38
 
0.4%
Other values (2150) 8213
95.1%

Length

2022-11-29T21:56:14.659647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-02-28 47
 
0.5%
2021-03-26 45
 
0.5%
2021-08-27 43
 
0.5%
2019-09-13 43
 
0.5%
2006-01-01 42
 
0.5%
2021-02-26 41
 
0.5%
2019-10-18 41
 
0.5%
2021-01-15 40
 
0.5%
2019-06-26 39
 
0.5%
2021-04-30 38
 
0.4%
Other values (2150) 8213
95.1%

Most occurring characters

ValueCountFrequency (%)
0 21012
24.9%
- 16667
19.7%
2 16627
19.7%
1 13642
16.1%
9 3716
 
4.4%
8 2376
 
2.8%
3 2357
 
2.8%
6 2245
 
2.7%
5 2081
 
2.5%
7 1969
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67862
80.3%
Dash Punctuation 16667
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21012
31.0%
2 16627
24.5%
1 13642
20.1%
9 3716
 
5.5%
8 2376
 
3.5%
3 2357
 
3.5%
6 2245
 
3.3%
5 2081
 
3.1%
7 1969
 
2.9%
4 1837
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 16667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21012
24.9%
- 16667
19.7%
2 16627
19.7%
1 13642
16.1%
9 3716
 
4.4%
8 2376
 
2.8%
3 2357
 
2.8%
6 2245
 
2.7%
5 2081
 
2.5%
7 1969
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21012
24.9%
- 16667
19.7%
2 16627
19.7%
1 13642
16.1%
9 3716
 
4.4%
8 2376
 
2.8%
3 2357
 
2.8%
6 2245
 
2.7%
5 2081
 
2.5%
7 1969
 
2.3%

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
False
7712 
True
920 
ValueCountFrequency (%)
False 7712
89.3%
True 920
 
10.7%
2022-11-29T21:56:15.110044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

popularity
Real number (ℝ)

Distinct94
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.102294
Minimum0
Maximum98
Zeros166
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2022-11-29T21:56:15.393101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q128
median39
Q351
95-th percentile70
Maximum98
Range98
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.515046
Coefficient of variation (CV)0.47350282
Kurtosis-0.22524095
Mean39.102294
Median Absolute Deviation (MAD)12
Skewness0.0018851425
Sum337531
Variance342.80694
MonotonicityNot monotonic
2022-11-29T21:56:16.003483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 233
 
2.7%
35 225
 
2.6%
40 222
 
2.6%
38 215
 
2.5%
39 214
 
2.5%
43 206
 
2.4%
41 204
 
2.4%
44 201
 
2.3%
37 198
 
2.3%
34 198
 
2.3%
Other values (84) 6516
75.5%
ValueCountFrequency (%)
0 166
1.9%
1 85
1.0%
2 43
 
0.5%
3 36
 
0.4%
4 54
 
0.6%
5 42
 
0.5%
6 40
 
0.5%
7 40
 
0.5%
8 52
 
0.6%
9 52
 
0.6%
ValueCountFrequency (%)
98 2
 
< 0.1%
96 1
 
< 0.1%
91 4
 
< 0.1%
90 2
 
< 0.1%
89 7
0.1%
88 4
 
< 0.1%
87 4
 
< 0.1%
86 9
0.1%
85 11
0.1%
84 14
0.2%

isrc
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8519
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size649.1 KiB
ITR007900051
 
4
FR8EU1800030
 
3
GBAYE9400055
 
3
QM38F1700004
 
2
UK7MC2200061
 
2
Other values (8514)
8618 

Length

Max length15
Median length12
Mean length12.005561
Min length12

Characters and Unicode

Total characters103632
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8410 ?
Unique (%)97.4%

Sample

1st rowGBBPW2000087
2nd rowGBXNG2015003
3rd rowUSLD91730821
4th rowUSUM72013244
5th rowUSUM72015141

Common Values

ValueCountFrequency (%)
ITR007900051 4
 
< 0.1%
FR8EU1800030 3
 
< 0.1%
GBAYE9400055 3
 
< 0.1%
QM38F1700004 2
 
< 0.1%
UK7MC2200061 2
 
< 0.1%
US2S71921056 2
 
< 0.1%
USSM11902498 2
 
< 0.1%
FR6P12100640 2
 
< 0.1%
GBKPL2144809 2
 
< 0.1%
DEC161503680 2
 
< 0.1%
Other values (8509) 8608
99.7%

Length

2022-11-29T21:56:16.322436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
itr007900051 4
 
< 0.1%
gbaye9400055 3
 
< 0.1%
fr8eu1800030 3
 
< 0.1%
tcacy1763698 2
 
< 0.1%
usjza1929924 2
 
< 0.1%
gbcvz1600144 2
 
< 0.1%
gbcvz0902722 2
 
< 0.1%
gbum71801115 2
 
< 0.1%
gbqcp1400118 2
 
< 0.1%
gbqcp1400109 2
 
< 0.1%
Other values (8509) 8608
99.7%

Most occurring characters

ValueCountFrequency (%)
0 18868
18.2%
1 11968
 
11.5%
2 7890
 
7.6%
9 4554
 
4.4%
3 4431
 
4.3%
7 4299
 
4.1%
5 4194
 
4.0%
6 4120
 
4.0%
U 3961
 
3.8%
4 3921
 
3.8%
Other values (45) 35426
34.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68120
65.7%
Uppercase Letter 35239
34.0%
Lowercase Letter 225
 
0.2%
Dash Punctuation 48
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 3961
 
11.2%
S 3551
 
10.1%
B 3276
 
9.3%
G 2874
 
8.2%
E 2193
 
6.2%
M 1947
 
5.5%
A 1909
 
5.4%
Q 1465
 
4.2%
C 1440
 
4.1%
R 1291
 
3.7%
Other values (16) 11332
32.2%
Lowercase Letter
ValueCountFrequency (%)
s 50
22.2%
u 50
22.2%
h 27
12.0%
c 19
 
8.4%
g 18
 
8.0%
m 17
 
7.6%
d 7
 
3.1%
y 7
 
3.1%
j 7
 
3.1%
x 4
 
1.8%
Other values (8) 19
 
8.4%
Decimal Number
ValueCountFrequency (%)
0 18868
27.7%
1 11968
17.6%
2 7890
11.6%
9 4554
 
6.7%
3 4431
 
6.5%
7 4299
 
6.3%
5 4194
 
6.2%
6 4120
 
6.0%
4 3921
 
5.8%
8 3875
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68168
65.8%
Latin 35464
34.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 3961
 
11.2%
S 3551
 
10.0%
B 3276
 
9.2%
G 2874
 
8.1%
E 2193
 
6.2%
M 1947
 
5.5%
A 1909
 
5.4%
Q 1465
 
4.1%
C 1440
 
4.1%
R 1291
 
3.6%
Other values (34) 11557
32.6%
Common
ValueCountFrequency (%)
0 18868
27.7%
1 11968
17.6%
2 7890
11.6%
9 4554
 
6.7%
3 4431
 
6.5%
7 4299
 
6.3%
5 4194
 
6.2%
6 4120
 
6.0%
4 3921
 
5.8%
8 3875
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18868
18.2%
1 11968
 
11.5%
2 7890
 
7.6%
9 4554
 
4.4%
3 4431
 
4.3%
7 4299
 
4.1%
5 4194
 
4.0%
6 4120
 
4.0%
U 3961
 
3.8%
4 3921
 
3.8%
Other values (45) 35426
34.2%

error
Categorical

CONSTANT
MISSING

Distinct1
Distinct (%)33.3%
Missing8629
Missing (%)> 99.9%
Memory size337.4 KiB
{'status': 404, 'message': 'analysis not found'}

Length

Max length48
Median length48
Mean length48
Min length48

Characters and Unicode

Total characters144
Distinct characters22
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row{'status': 404, 'message': 'analysis not found'}
2nd row{'status': 404, 'message': 'analysis not found'}
3rd row{'status': 404, 'message': 'analysis not found'}

Common Values

ValueCountFrequency (%)
{'status': 404, 'message': 'analysis not found'} 3
 
< 0.1%
(Missing) 8629
> 99.9%

Length

2022-11-29T21:56:16.638845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T21:56:17.117790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
status 3
16.7%
404 3
16.7%
message 3
16.7%
analysis 3
16.7%
not 3
16.7%
found 3
16.7%

Most occurring characters

ValueCountFrequency (%)
s 18
12.5%
' 18
12.5%
15
 
10.4%
a 12
 
8.3%
t 9
 
6.2%
n 9
 
6.2%
u 6
 
4.2%
: 6
 
4.2%
4 6
 
4.2%
e 6
 
4.2%
Other values (12) 39
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87
60.4%
Other Punctuation 27
 
18.8%
Space Separator 15
 
10.4%
Decimal Number 9
 
6.2%
Open Punctuation 3
 
2.1%
Close Punctuation 3
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 18
20.7%
a 12
13.8%
t 9
10.3%
n 9
10.3%
u 6
 
6.9%
e 6
 
6.9%
o 6
 
6.9%
y 3
 
3.4%
d 3
 
3.4%
f 3
 
3.4%
Other values (4) 12
13.8%
Other Punctuation
ValueCountFrequency (%)
' 18
66.7%
: 6
 
22.2%
, 3
 
11.1%
Decimal Number
ValueCountFrequency (%)
4 6
66.7%
0 3
33.3%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
{ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
} 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87
60.4%
Common 57
39.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 18
20.7%
a 12
13.8%
t 9
10.3%
n 9
10.3%
u 6
 
6.9%
e 6
 
6.9%
o 6
 
6.9%
y 3
 
3.4%
d 3
 
3.4%
f 3
 
3.4%
Other values (4) 12
13.8%
Common
ValueCountFrequency (%)
' 18
31.6%
15
26.3%
: 6
 
10.5%
4 6
 
10.5%
{ 3
 
5.3%
, 3
 
5.3%
0 3
 
5.3%
} 3
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 18
12.5%
' 18
12.5%
15
 
10.4%
a 12
 
8.3%
t 9
 
6.2%
n 9
 
6.2%
u 6
 
4.2%
: 6
 
4.2%
4 6
 
4.2%
e 6
 
4.2%
Other values (12) 39
27.1%

Interactions

2022-11-29T21:55:51.349660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:54:58.193987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:03.547957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:07.642702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:10.965620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:14.271742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:17.601644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:20.794048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:24.261430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:27.856720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:32.011268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:36.663021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:41.243889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:46.844784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:51.643422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:54:58.487739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:03.922472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:07.900512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:11.192408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:14.493182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:17.827485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:21.028342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:24.532975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:28.084781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:32.337455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:37.012103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:41.591102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:47.138008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:51.911627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:54:58.837554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:04.504696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:08.150198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:11.436710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:14.744704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:18.021421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:21.287468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:24.750610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:28.330517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:32.637195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:37.356487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:41.876578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:47.450928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:52.345155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:54:59.113497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:04.912477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:08.356722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:11.637849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:14.981396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:18.231867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:21.519140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:24.968588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:28.596932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:32.931818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:37.680421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:42.410905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:47.794856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:52.656141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:54:59.576028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:05.204921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:08.576010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:11.863610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:15.233741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:18.450192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:21.753002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:25.218830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:28.860236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:33.231180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:37.992592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:42.771541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:48.047677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:52.995743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:54:59.818013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:05.532175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:08.829108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:12.112796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:15.462570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:18.673712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:21.986708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:25.461898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:29.185074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:33.525315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:38.320324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:43.083051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:48.304975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:53.256917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:00.190984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:05.782123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:09.049176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:12.341954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:15.665670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:18.916574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:22.217638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:25.709331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:29.454044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:33.858173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:38.602809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:43.402415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:48.568450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:53.537910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:00.756409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:06.017141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:09.410280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:12.602350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:15.906580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:19.138387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:22.437155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:25.975323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:29.742683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:34.204643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:38.930070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:43.859761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:48.878906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:53.844466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:01.215146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:06.264691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:09.637146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:12.835231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:16.150427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:19.368778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:22.666350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:26.237796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:30.010237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:34.633877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:39.247229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:44.292048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:49.233957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:54.196781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:01.670618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:06.532424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:09.859892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:13.071926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:16.529358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:19.589199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:22.933299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:26.547286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:30.297768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:34.979631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:39.547383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:44.993048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:49.573108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:54.483004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:02.174372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:06.775644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:10.072014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:13.307058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:16.753819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:19.836655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:23.168776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:26.799079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:30.615880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:35.337572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:39.877322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:45.523221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:49.853598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:54.792348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:02.571811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:06.990634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:10.312412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:13.543741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:16.943861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:20.064700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:23.533296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:27.074617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:31.058957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:35.694102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:40.155761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:45.889315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:50.155468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:55.215940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:02.834567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:07.202964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:10.557039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:13.803045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:17.158168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:20.346645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:23.758350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:27.358747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:31.339745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:36.018418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:40.678148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:46.191225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:50.461164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:55.546466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:03.219405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:07.416277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:10.753678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:14.046860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:17.386600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:20.579049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:23.961000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:27.613329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:31.703097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:36.377238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:40.972644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:46.532997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T21:55:50.951471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-29T21:56:17.347723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T21:56:17.798004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T21:56:18.226297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T21:56:18.734708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T21:56:19.139437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T21:56:19.450554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T21:55:56.102637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T21:55:57.647098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-29T21:55:58.440404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0artist_nametrack_nameplayshashdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeiduritrack_hrefanalysis_urlduration_mstime_signatureid_hashalbumrelease_dateexplicitpopularityisrcerror
01!!!So We Can Fuck0.69314740826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f7170.8090.4669.0-9.4910.00.0744580.0000720.6146450.1097510.7200120.002track4knd2gQyr2DTRLfJDHcyMSspotify:track:4knd2gQyr2DTRLfJDHcyMShttps://api.spotify.com/v1/tracks/4knd2gQyr2DTRLfJDHcyMShttps://api.spotify.com/v1/audio-analysis/4knd2gQyr2DTRLfJDHcyMS2965004.040826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717I'm Sick of This/So We Can Fuck2020-05-01False28GBBPW2000087NaN
1500110100 010101000000 871 00031.0986128fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd1336940.3950.2602.0-16.1530.00.0468840.5584720.6141040.1088540.0724152.546track7ns3vcnzAxjCZVYwlwazahspotify:track:7ns3vcnzAxjCZVYwlwazahhttps://api.spotify.com/v1/tracks/7ns3vcnzAxjCZVYwlwazahhttps://api.spotify.com/v1/audio-analysis/7ns3vcnzAxjCZVYwlwazah754173.08fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd1336948712020-12-25False33GBXNG2015003NaN
2603 GreedoSubstance (We Woke Up)1.3862945139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a30.7300.5403.0-5.9790.00.0506930.3096880.0000000.1484200.3270141.954track2S8gTIectkC846PHdsAshCspotify:track:2S8gTIectkC846PHdsAshChttps://api.spotify.com/v1/tracks/2S8gTIectkC846PHdsAshChttps://api.spotify.com/v1/audio-analysis/2S8gTIectkC846PHdsAshC2366194.05139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a3Substance (We Woke Up)2021-02-03True62USLD91730821NaN
37070 ShakeGuilty Conscience - Tame Impala Remix1.0986122b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd5600.4150.8781.0-3.6500.00.2776320.0548670.0034740.2004890.3090191.777track5i5fCpsnqDJ9AfeObgd0gWspotify:track:5i5fCpsnqDJ9AfeObgd0gWhttps://api.spotify.com/v1/tracks/5i5fCpsnqDJ9AfeObgd0gWhttps://api.spotify.com/v1/audio-analysis/5i5fCpsnqDJ9AfeObgd0gW2149864.02b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560Guilty Conscience (Tame Impala Remix)2020-07-24False60USUM72013244NaN
48070 ShakeGuilty Conscience - Tame Impala Remix Extended0.693147283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a40.4260.8509.0-4.7541.00.2358620.0703650.0062700.1371500.2130191.928track7qDUOLnOLYKTwzvCJDnYRfspotify:track:7qDUOLnOLYKTwzvCJDnYRfhttps://api.spotify.com/v1/tracks/7qDUOLnOLYKTwzvCJDnYRfhttps://api.spotify.com/v1/audio-analysis/7qDUOLnOLYKTwzvCJDnYRf2870664.0283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4Guilty Conscience (Tame Impala Remix)2020-07-24False38USUM72015141NaN
59070 ShakeGuilty Conscience - Tame Impala Remix Instrumental0.693147ab6360e389e3f5d1195762455462751a2cafccfcb6ad56ddbf025a8c999b0b730.5180.8159.0-7.0161.00.0898410.0729720.6386910.1612680.0716191.862track2nwc1w2yyOXPCyTaphRQGNspotify:track:2nwc1w2yyOXPCyTaphRQGNhttps://api.spotify.com/v1/tracks/2nwc1w2yyOXPCyTaphRQGNhttps://api.spotify.com/v1/audio-analysis/2nwc1w2yyOXPCyTaphRQGN2859334.0ab6360e389e3f5d1195762455462751a2cafccfcb6ad56ddbf025a8c999b0b73Guilty Conscience (Tame Impala Remix)2020-07-24False33USUM72015142NaN
610070 ShakeThe Pines0.6931473c8a3f0b83fbbf1283e05bc8e8359c3b2e0167a2846aeae1ab32a3b823b9b7dd0.5220.4399.0-7.0370.00.0339180.0339180.0000090.3534700.1760163.927track0uTw7TNnYn64XmCAo5jr0cspotify:track:0uTw7TNnYn64XmCAo5jr0chttps://api.spotify.com/v1/tracks/0uTw7TNnYn64XmCAo5jr0chttps://api.spotify.com/v1/audio-analysis/0uTw7TNnYn64XmCAo5jr0c2136134.03c8a3f0b83fbbf1283e05bc8e8359c3b2e0167a2846aeae1ab32a3b823b9b7ddModus Vivendi2020-01-17False46USUM71925530NaN
711100 gecs800db cloud0.6931475a4fe1451e61705a1b59338ee79fb383eb3efd7f57d41dc32eb2f56f1422a4460.4340.8468.0-5.3101.00.3987760.1292720.0000160.1266330.4880141.617track5N7X3lGWDi4P0v2h9Vs9mFspotify:track:5N7X3lGWDi4P0v2h9Vs9mFhttps://api.spotify.com/v1/tracks/5N7X3lGWDi4P0v2h9Vs9mFhttps://api.spotify.com/v1/audio-analysis/5N7X3lGWDi4P0v2h9Vs9mF1039404.05a4fe1451e61705a1b59338ee79fb383eb3efd7f57d41dc32eb2f56f1422a4461000 gecs and The Tree of Clues2020-07-10False34USAT22000181NaN
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913100 gecshand crushed by a mallet3.713572cbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c850.6590.4731.0-7.3060.00.1177830.2421620.0000000.2468600.788084.505track7CUkeiG7QtB7tPU9f8SANSspotify:track:7CUkeiG7QtB7tPU9f8SANShttps://api.spotify.com/v1/tracks/7CUkeiG7QtB7tPU9f8SANShttps://api.spotify.com/v1/audio-analysis/7CUkeiG7QtB7tPU9f8SANS1266164.0cbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c851000 gecs2019-05-31True61USAT22000422NaN
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86239074AuðnÞjáning Heillar Þjóðar0.0330af1ff5368f313c7ef78fd95d8eb1c67993f72f47dcf7c3c623da425fd349f0.1530.6081.0-12.2620.00.0381620.0001650.6113950.1124350.0365100.018track7tRTy6peaaYpdUPBuuG48bspotify:track:7tRTy6peaaYpdUPBuuG48bhttps://api.spotify.com/v1/tracks/7tRTy6peaaYpdUPBuuG48bhttps://api.spotify.com/v1/audio-analysis/7tRTy6peaaYpdUPBuuG48b3107264.0330af1ff5368f313c7ef78fd95d8eb1c67993f72f47dcf7c3c623da425fd349fAuðn2015-11-04False22QM2PV1559440NaN
86249075PornofilmyМолодежный бунт0.090cf8fb9d9d9ed5177ed2609c5ec36542c363b39ce4602af7ca3c57e5d6549fb0.5840.5467.0-6.4731.00.1213320.0245950.0005300.0944920.552080.921track20Gfb7tnqty0P2OZZHoMuLspotify:track:20Gfb7tnqty0P2OZZHoMuLhttps://api.spotify.com/v1/tracks/20Gfb7tnqty0P2OZZHoMuLhttps://api.spotify.com/v1/audio-analysis/20Gfb7tnqty0P2OZZHoMuL2883334.090cf8fb9d9d9ed5177ed2609c5ec36542c363b39ce4602af7ca3c57e5d6549fbРусская мечта. Часть 12015-09-28True28SMRUS0022309NaN
86259076БарбитурныйЭкватор0.0e930b687bbc4a7fae88d1225a7d6c75f62fbd13d0f4ee7936651340ecb7925ce0.9530.4291.0-9.1971.00.3052760.0030750.0139030.1034590.0789110.010track3QVxaX9eb8t3QHvxxKKGNYspotify:track:3QVxaX9eb8t3QHvxxKKGNYhttps://api.spotify.com/v1/tracks/3QVxaX9eb8t3QHvxxKKGNYhttps://api.spotify.com/v1/audio-analysis/3QVxaX9eb8t3QHvxxKKGNY1920264.0e930b687bbc4a7fae88d1225a7d6c75f62fbd13d0f4ee7936651340ecb7925ceЧЧ2020-07-21True13FRX282004226NaN
86269077Alfredo OlivasEl Problema - Versión Banda0.0ae9b0a36ea419c805400bff55e898ab2d27b6161a72f0ead2a1a809574d9f9a50.7180.6507.0-4.0931.00.0435380.1996700.0000020.1160040.9750149.769track30pKCtnq9axMXlyLat7Rnuspotify:track:30pKCtnq9axMXlyLat7Rnuhttps://api.spotify.com/v1/tracks/30pKCtnq9axMXlyLat7Rnuhttps://api.spotify.com/v1/audio-analysis/30pKCtnq9axMXlyLat7Rnu1958933.0ae9b0a36ea419c805400bff55e898ab2d27b6161a72f0ead2a1a809574d9f9a5La Rueda De La Fortuna2017-08-18False69QM7RD1600028NaN
86279078艾玲玲白面书生 - 成语故事0.0de417044ab37e36d92e3516e6a6e356e2a177bb4002ebcb0a1bfbdd755d270bb0.6470.44710.0-8.5241.00.3393250.6841060.0000000.0433470.604089.456track03eSBByFWmLwgqX7TxRrrbspotify:track:03eSBByFWmLwgqX7TxRrrbhttps://api.spotify.com/v1/tracks/03eSBByFWmLwgqX7TxRrrbhttps://api.spotify.com/v1/audio-analysis/03eSBByFWmLwgqX7TxRrrb2255515.0de417044ab37e36d92e3516e6a6e356e2a177bb4002ebcb0a1bfbdd755d270bb中国少儿成语故事大全(一)2022-04-06False0FR59R2258223NaN
86289079时髦 婴儿睡觉音乐分类(音乐)0.06c3fd24188e2583f5231edafb564dffe56a0ad0168bc68c08ee89a0b90663e470.7520.11410.0-16.4111.00.0691530.6709020.6429060.1043600.1900124.014track3VgNf9XKnV3koqMFXgkTWaspotify:track:3VgNf9XKnV3koqMFXgkTWahttps://api.spotify.com/v1/tracks/3VgNf9XKnV3koqMFXgkTWahttps://api.spotify.com/v1/audio-analysis/3VgNf9XKnV3koqMFXgkTWa1394124.06c3fd24188e2583f5231edafb564dffe56a0ad0168bc68c08ee89a0b90663e47睡眠(分类)2021-02-09False0QZFQ58975216NaN
86299080Hilang ChildLoss0.01a89d3fe25f6e6debf7724e2aada51c3f2532232d645eb186e04621ee2783ef00.1640.5877.0-12.3940.00.0524980.2247420.5458070.3852620.077581.777track78iatjUprJbkk0THCcMUcaspotify:track:78iatjUprJbkk0THCcMUcahttps://api.spotify.com/v1/tracks/78iatjUprJbkk0THCcMUcahttps://api.spotify.com/v1/audio-analysis/78iatjUprJbkk0THCcMUca2380005.01a89d3fe25f6e6debf7724e2aada51c3f2532232d645eb186e04621ee2783ef0Seimbang / Balance2021-11-19False0GBKPL2169433NaN
86309081Lee Yun Jung엄마배가 풍선만해 졌어요 - Melody Version0.08cb24c547c10b8be489ebc24044ed4600598a7cf8cff408e66e50a587aad5ee70.8110.4800.0-11.3181.00.1034590.1930970.5833320.0737150.8270124.004track6vzXFOXK5AhoARJC4DjsY8spotify:track:6vzXFOXK5AhoARJC4DjsY8https://api.spotify.com/v1/tracks/6vzXFOXK5AhoARJC4DjsY8https://api.spotify.com/v1/audio-analysis/6vzXFOXK5AhoARJC4DjsY81302984.08cb24c547c10b8be489ebc24044ed4600598a7cf8cff408e66e50a587aad5ee7가족-엄마배가 풍선만해 졌어요2018-05-14False0KRZ291811549NaN
86319082MasterGarageMedley Lecce (Mix parody)0.0c59a37bb9ee00ae27ad1aa0e43e566d3b32db11d0c130daea575a3c4f17718eb0.4110.8586.0-3.5840.00.2004890.0804730.0000000.6402740.862090.385track6NFcMPxPcqtVNi2Sfr0vf0spotify:track:6NFcMPxPcqtVNi2Sfr0vf0https://api.spotify.com/v1/tracks/6NFcMPxPcqtVNi2Sfr0vf0https://api.spotify.com/v1/audio-analysis/6NFcMPxPcqtVNi2Sfr0vf02535604.0c59a37bb9ee00ae27ad1aa0e43e566d3b32db11d0c130daea575a3c4f17718ebThe Lesionati2021-06-30False3US3DF2193423NaN

Duplicate rows

Most frequently occurring

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08066Umberto TozziGloria1.38629423134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c82930.7840.6774.0-12.3011.00.0457380.1552930.0000120.2062010.879131.618track35tzxthMBglBMjmZ7Fn1hjspotify:track:35tzxthMBglBMjmZ7Fn1hjhttps://api.spotify.com/v1/tracks/35tzxthMBglBMjmZ7Fn1hjhttps://api.spotify.com/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj2646744.023134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293Gloria1979-01-01False63ITR007900051NaN2
18871Umberto TozziGloria0.00000023134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c82930.7840.6774.0-12.3011.00.0457380.1552930.0000120.2062010.879131.618track35tzxthMBglBMjmZ7Fn1hjspotify:track:35tzxthMBglBMjmZ7Fn1hjhttps://api.spotify.com/v1/tracks/35tzxthMBglBMjmZ7Fn1hjhttps://api.spotify.com/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj2646744.023134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293Gloria1979-01-01False63ITR007900051NaN2